To see the other types of publications on this topic, follow the link: Rear-end collision avoidance.

Journal articles on the topic 'Rear-end collision avoidance'

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Rear-end collision avoidance.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Chen, Dong Xue, Lei He, Chang Fu Zong, Hong Yu Zheng, and Zheng Tang Shi. "Study on Modeling and Simulation of Vehicle Safety Distance in Low Speed for Rear-end Collision Avoidance." Advanced Materials Research 694-697 (May 2013): 3375–80. http://dx.doi.org/10.4028/www.scientific.net/amr.694-697.3375.

Full text
Abstract:
Rear-end collisions of low speed vehicles on the urban road are common accident scenarios. A judgment of safety distance is a crucial part of a rear-end collision avoidance system which relies on safety distance model. In order to solve the problems of traditional safety distance models, this paper proposes a new model to avoid rear-end collisions. The new model considers the braking performance of the host vehicle and the motion state of the heading vehicle. Then a rear-end collision avoidance system including the new model is established using a combination of CarSim with Simulink software. Three typical working conditions through the co-simulation is simulated for verifying the model. Simulation results show that the new model ensures rear-end collision can be avoided, meanwhile keeping a short relative distance that improves road efficiency.
APA, Harvard, Vancouver, ISO, and other styles
2

Sun, Bin. "Based on Rear-End Morphological Analysis of Collision Avoidance Algorithm." Applied Mechanics and Materials 321-324 (June 2013): 1522–28. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.1522.

Full text
Abstract:
Rear-end collision is one kind of traffic accident with extreme danger. Peoples security and property are serious threatened by this kind of accidents. Based on morphological research of real-world collisions, this paper makes quantitative analysis of minimum safe distance under complex traffic situations. A novel mathematics model and corresponding collision avoidance algorithms are proposed. To verify feasibility of this algorithm, this paper performs accident emulation and data analysis with Visual Basic 6.0. Experimental results show that this approach is capable of reducing occurrence possibility of rear-end collisions.
APA, Harvard, Vancouver, ISO, and other styles
3

Bian, Chentong, Guodong Yin, Liwei Xu, and Ning Zhang. "REAR-END COLLISION ESCAPE ALGORITHM FOR INTELLIGENT VEHICLES SUPPORTED BY VEHICULAR COMMUNICATION." Transport 37, no. 6 (2022): 398–410. http://dx.doi.org/10.3846/transport.2022.18172.

Full text
Abstract:
To reduce rear-end collision risks and improve traffic safety, a novel rear-end collision escape algorithm is proposed for intelligent vehicles supported by vehicular communication. Numerous research has been carried out on rear-end collision avoidance. Most of these studies focused on maintaining a safe front clearance of a vehicle while only few considered the vehicle’s rear clearance. However, an intelligent vehicle may be collided by a following vehicle due to wrong manoeuvres of an unskilled driver of the following vehicle. Hence, it is essential for an intelligent vehicle to maintain a safe rear clearance when there is potential for a rear-end collision caused by a following vehicle. In this study, a rear-end collision escape algorithm is proposed to prevent rear-end collisions by a following vehicle considering both straight and curved roads. A trajectory planning method is designed according to the motions of the considered intelligent vehicle and the corresponding adjacent vehicles. The successive linearization and the Model Predictive Control (MPC) algorithms are used to design a motion controller in the proposed algorithm. Simulations were performed to demonstrate the effectiveness of the proposed algorithm. The results show that the proposed algorithm is effective in preventing rear-end collisions caused by a following vehicle.
APA, Harvard, Vancouver, ISO, and other styles
4

Behbahani, Hamid, Navid Nadimi, Hooman Alenoori, and Mina Sayadi. "Developing a New Surrogate Safety Indicator Based on Motion Equations." PROMET - Traffic&Transportation 26, no. 5 (2014): 371–81. http://dx.doi.org/10.7307/ptt.v26i5.1388.

Full text
Abstract:
Collision avoidance system (CAS), with the help of surrogate safety measures is a beneficial tool for reducing driver errors and preventing rear-end collisions. One of the most well-known surrogate safety measures to detect rear-end conflicts is Time-to-collision (TTC). TTC refers to the time remaining before the rear-end accident if the course and the speed of vehicles are maintained constant. Different surrogate measures have been derived from TTC; however, the most important are Time Exposed Time-to-collision (TET) and Time Integrated Time-to-collision (TIT). In this paper a new surrogate safety measure based on TTC notion has been developed. This new indicator merges TET and TIT into one measure and gives a score between 0 and 100%, as the probability of collision. Applying this indicator in CAS as a safety measure will be more useful than TET&TIT, to reduce driver errors and rear-end collisions.
APA, Harvard, Vancouver, ISO, and other styles
5

Brown, Timothy L., John D. Lee, and Daniel V. McGehee. "Attention-Based Model of Driver Performance in Rear-End Collisions." Transportation Research Record: Journal of the Transportation Research Board 1724, no. 1 (2000): 14–20. http://dx.doi.org/10.3141/1724-03.

Full text
Abstract:
Several driver-performance factors contribute to rear-end collisions—driver inattention, perception-reaction time, and limitations of the human visual system. Although many evaluations have examined driver response to various rear-end collision avoidance systems (RECAS) display and algorithm alternatives, little research has been directed at creating a quantitative model of driver performance to evaluate these alternatives. Current considerations of driver behavior in developing warning algorithms tend to ignore the fundamental problem of driver inattention and assume a fixed driver reaction time with no further adjustment after the initial response. A more refined model of driver response to rear-end crash scenarios can identify more appropriate and timely information to be displayed to the driver. An attention-based rear-end collision avoidance model (ARCAM) is introduced that describes the driver’s attention distribution, information extraction and judgment process, and the reaction process. ARCAM predicts the closed-loop nature of collision response performance and explains how the driver might use RECAS warnings.
APA, Harvard, Vancouver, ISO, and other styles
6

Araki, H. "Development of rear-end collision avoidance system." JSAE Review 18, no. 3 (1997): 314–16. http://dx.doi.org/10.1016/s0389-4304(97)00010-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Takubo, N. "Evaluation of rear-end collision avoidance system." JSAE Review 16, no. 3 (1995): 324–25. http://dx.doi.org/10.1016/0389-4304(95)95164-p.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sun, Rui, Fei Xie, Dabin Xue, Yucheng Zhang, and Washington Yotto Ochieng. "A Novel Rear-End Collision Detection Algorithm Based on GNSS Fusion and ANFIS." Journal of Advanced Transportation 2017 (2017): 1–10. http://dx.doi.org/10.1155/2017/9620831.

Full text
Abstract:
Rear-end collisions are one of the most common types of accidents on roads. Global Satellite Navigation Systems (GNSS) have recently become sufficiently flexible and cost-effective in order to have great potential for use in rear-end collision avoidance systems (CAS). Nevertheless, there are two main issues associated with current vehicle rear-end CAS: (1) achieving relative vehicle positioning and dynamic parameters with sufficiently high accuracy and (2) a reliable method to extract the car-following status from such information. This paper introduces a novel integrated algorithm for rear-end collision detection. Access to high accuracy positioning is enabled by GNSS, electronic compass, and lane information fusion with Cubature Kalman Filter (CKF). The judgment of the car-following status is based on the application of the Adaptive Neurofuzzy Inference System (ANFIS). The field test results show that the designed algorithm could effectively detect rear-end collisions with an accuracy of 99.61% and a false alarm rate of 5.26% in the 10 Hz output rate.
APA, Harvard, Vancouver, ISO, and other styles
9

DeLucia, Patricia R., and Anand Tharanathan. "Effects of Optical Flow and Discrete Warnings on Deceleration Detection during Car Following." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 49, no. 17 (2005): 1673–76. http://dx.doi.org/10.1177/154193120504901737.

Full text
Abstract:
Tau specifies time-to-contact between a driver and a lead car, and is potentially useful to prevent rear-end collisions. However, studies suggest that time-to-contact judgments are based on multiple information sources and that effective information varies with distance. We focused on three questions: Does a driver's response to a lead car's deceleration occur when the car's optical size, expansion rate, or tau reaches a “critical” value? Does effective information differ for near and far lead cars? Is a driver's response affected by discrete warnings (brake lights and auditory warnings) that occur independently of optical flow information? Results suggested that responses were not based on a critical value of the optical parameters considered here, and were affected by discrete warnings. Further, effective information varied with the distance and deceleration rate of the lead car. Results were consistent with prior proposals that advanced brake warning systems and collision-avoidance warning systems can reduce the incidence of rear-end collisions. Future studies of this kind will help to improve the design of collision-avoidance systems and to reduce rear-end collisions.
APA, Harvard, Vancouver, ISO, and other styles
10

Razzaq, Sheeba, Amil Roohani Dar, Munam Ali Shah, et al. "Multi-Factor Rear-End Collision Avoidance in Connected Autonomous Vehicles." Applied Sciences 12, no. 3 (2022): 1049. http://dx.doi.org/10.3390/app12031049.

Full text
Abstract:
According to World Health Organization (WHO), the leading cause of fatalities and injuries is rear-ending collision in vehicles. The critical challenge of the technologically rich transportation system is to reduce the chances of accidents between vehicles. For this purpose, it is especially important to analyze the factors that are the cause of accidents. Based on these factors’ results, this paper presents a driver assistance system for collision avoidance. There are many factors involved in collisions in the existing literature from which we identified some factors which can affect the accident occurrence probability. However, with advancements in the technologies of autonomous vehicles, these factors can be controlled using an onboard driver assistance system. We used MATLAB’s Fuzzy Inference System Tool to analyze the categories of accident contributing factors. Fuzzy results are validated using the VOMAS agent in the NetLogo simulation model. The proposed system can inform the vehicle’s automated system when chances of an accident are higher so that the vehicle may take control from the driver. The proposed research is extremely helpful in handling various kinds of factors involved in accidents. The results of the experiments demonstrated that multi-factor-enabled vehicles could better avoid collision as compared to other vehicles.
APA, Harvard, Vancouver, ISO, and other styles
11

Butt, Muhammad A., Faisal Riaz, Yasir Mehmood, and Somyyia Akram. "REEEC-AGENT: human driver cognition and emotions-inspired rear-end collision avoidance method for autonomous vehicles." SIMULATION 97, no. 9 (2021): 601–17. http://dx.doi.org/10.1177/00375497211004721.

Full text
Abstract:
Rear-end collision detection and avoidance is one of the most crucial driving tasks of self-driving vehicles. Mathematical models and fuzzy logic-based methods have recently been proposed to improve the effectiveness of the rear-end collision detection and avoidance systems in autonomous vehicles (AVs). However, these methodologies do not tackle real-time object detection and response problems in dense/dynamic road traffic conditions due to their complex computation and decision-making structures. In our previous work, we presented an affective computing-inspired Enhanced Emotion Enabled Cognitive Agent (EEEC_Agent), which is capable of rear-end collision avoidance using artificial human driver emotions. However, the architecture of the EEEC_Agent is based on an ultrasonic sensory system which follows three-state driving strategies without considering the neighbor vehicles types. To address these issues, in this paper we propose an extended version of the EEEC_Agent which contains human driver-inspired dynamic driving mode controls for autonomous vehicles. In addition, a novel end-to-end learning-based motion planner has been devised to perceive the surrounding environment and regulate driving tasks accordingly. The real-time in-field experiments performed using a prototype AV demonstrate the effectiveness of this proposed rear-end collision avoidance system.
APA, Harvard, Vancouver, ISO, and other styles
12

Xu, Qingwei, Xiangyang Lu, and Juncai Xu. "Optimized Active Collision Avoidance Algorithm of Intelligent Vehicles." Electronics 12, no. 11 (2023): 2451. http://dx.doi.org/10.3390/electronics12112451.

Full text
Abstract:
This research introduces an innovative strategy to impede and lessen lateral and rear-end vehicular collisions by consolidating braking systems with active emergency steering controls. This study puts forward a T-type active emergency steering method, designed to circumvent both lateral and rear-end collisions at vehicular intersections. To secure vehicular stability and condense the time required for steering during the T-type active emergency process, this research formulates a nonlinear dynamic model for the vehicle, in addition to a nonlinear tire model. This study also engages in a thorough analysis of the constraints linked to the initial and terminal states of the steering process. The issue at hand is articulated as an optimization control problem with boundary value restrictions, which is subsequently resolved using the Radau pseudospectral method. Simulation results corroborate that the prompt commencement of the anti-collision strategy can effectively deter potential collisions. This pioneering approach shows considerable promise in augmenting the active safety of intelligent vehicles and bears meaningful implications for high-precision automotive collision evasion systems.
APA, Harvard, Vancouver, ISO, and other styles
13

Xue, Qingwan, Xuedong Yan, Xiaomeng Li, and Yun Wang. "Uncertainty Analysis of Rear-End Collision Risk Based on Car-Following Driving Simulation Experiments." Discrete Dynamics in Nature and Society 2018 (September 20, 2018): 1–13. http://dx.doi.org/10.1155/2018/5861249.

Full text
Abstract:
Rear-end collisions are one of the most common types of accidents, and the importance of examining rear-end collisions has been demonstrated by numerous accidents analysis researches. Over the past decades, lots of models have been built to describe driving behaviour during car following to better understand the cause of collisions. However, it is necessary to consider individual difference in car-following modelling while it seems to be ignored in most of previous models. In this study, a rear-end collision avoidance behaviour model considering drivers’ individual differences was developed based on a common deceleration pattern extracted from driving behaviour data, which were collected in a car-following driving simulation experiment. Parameters of variables in the model were calibrated by liner regression and Monte Carlo method was adopted in model simulation for uncertainty analysis. Simulation results confirmed the effectiveness of this model by comparing them to the experiment data and the influence of driving speed and headway distance on the rear-end collision risk was indicated as well. The thresholds for driving speed and headway distance were 18 m/s and 15 m, respectively. An obvious increase of collision risk was observed according to the simulation results.
APA, Harvard, Vancouver, ISO, and other styles
14

Kim, Dae-Jin, Kwang-Hyun Park, and Zeungnam Bien. "Hierarchical Longitudinal Controller for Rear-End Collision Avoidance." IEEE Transactions on Industrial Electronics 54, no. 2 (2007): 805–17. http://dx.doi.org/10.1109/tie.2007.891660.

Full text
APA, Harvard, Vancouver, ISO, and other styles
15

Uc, Ergun Y., Matthew Rizzo, Steven W. Anderson, Qian Shi, and Jeffrey D. Dawson. "Unsafe rear-end collision avoidance in Alzheimer's disease." Journal of the Neurological Sciences 251, no. 1-2 (2006): 35–43. http://dx.doi.org/10.1016/j.jns.2006.08.011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
16

Belkadi, Y., and A. D. Campbell. "Naturalistic Driver Behaviour in Response to the Multi-Sensory Experience of Rear-End Collisions." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 474–78. http://dx.doi.org/10.1177/1541931213601602.

Full text
Abstract:
The purpose of this study was to quantify naturalistic driver behaviour during real-world rear-end collisions. Rear-end collisions from the 100-car naturalistic driving database were reviewed and behaviour of drivers in the struck vehicles (i.e., lead vehicles) were analyzed. Results indicate that rear-ended drivers disengage their foot from the brake pedal and then reapply within average perception and response intervals (0.90 seconds; 0.15 SD) and braking intensities (0.50 g; 0.27 SD) consistent with visual detection of immediate collision hazards, despite the added complexity of a multi-sensory collision experience. Together with previous research, these data suggest that perception and response durations and braking intensities are scaled to the severity of the detected hazard. This research has applications to forensic investigations of collisions in which the inclusion of driver behaviour data is a requisite to quantifying impact dynamics and the analysis of collision avoidance potential in real-world conditions.
APA, Harvard, Vancouver, ISO, and other styles
17

Li, Lu Xi, Yao Deng, Yue Chen, and Jian Hua Shen. "A VLC Based Vehicle Collision Avoidance Scheme for Intelligent Transport Systems." Applied Mechanics and Materials 713-715 (January 2015): 1175–79. http://dx.doi.org/10.4028/www.scientific.net/amm.713-715.1175.

Full text
Abstract:
Visible Light Communication (VLC) technology is one of the main candidate solutions for Intelligent Transportation Systems (ITS). This paper presents a comprehensive study on VLC based anti-rear-end collision scheme on the highway. A detailed VLC based rear-end collision avoidance model is proposed with theoretical analysis and numerical simulations.
APA, Harvard, Vancouver, ISO, and other styles
18

Riaz, Faisal, and Muaz A. Niazi. "Enhanced emotion enabled cognitive agent-based rear-end collision avoidance controller for autonomous vehicles." SIMULATION 94, no. 11 (2017): 957–77. http://dx.doi.org/10.1177/0037549717742203.

Full text
Abstract:
Amongst collisions, rear-end collisions are the deadliest. Several rear-end collision avoidance solutions have been proposed recently in the literature. A key problem with existing solutions is their dependence on precise mathematical models. However, real world driving is influenced by a number of nonlinear factors. These include road surface conditions, driver reaction time, pedestrian flow, and vehicle dynamics. These factors involve so many different variations that precise mathematical solutions are hard to obtain, if not impossible. This problem with precise control-based rear-end collision avoidance schemes has also previously been addressed using fuzzy logic, but the excessive number of fuzzy rules straightforwardly prejudices their efficiency. Furthermore, such fuzzy logic-based controllers have been proposed without the use of an appropriate modeling technique. One such modeling technique is agent-based modeling. This technique is suitable because it allows for mimicking the functions of an artificial human driver executing fuzzy rules. Keeping in view these limitations, we propose an enhanced emotion enabled cognitive agent (EEEC_Agent)-based controller. The proposed EEEC_Agent helps autonomous vehicles (AVs) avoid rear-end collisions with fewer rules. One key innovation in its design is to use the human emotion of fear. The resultant agent is very efficient and also uses the Ortony–Clore–Collins (OCC) model. The fear generation mechanism of EEEC_Agent is verified through NetLogo simulation. Furthermore, practical validation of EEEC_Agent functions is performed by using a specially built prototype AV platform. Finally, a qualitative comparison with existing state-of-the-art research works reflects that the proposed model outperforms recent research proposals.
APA, Harvard, Vancouver, ISO, and other styles
19

Johnson, Sandra, Mohammed Fayaz A, and Hari Krishnan S. "IoT based rear-end collision avoidance system in highways." International Journal of Advanced Computer Research 9, no. 45 (2019): 379–85. http://dx.doi.org/10.19101/ijacr.2019.940067.

Full text
APA, Harvard, Vancouver, ISO, and other styles
20

Xiang, Yu, Sida Huang, Min Li, Jin Li, and Wenyong Wang. "Rear-End Collision Avoidance-Based on Multi-Channel Detection." IEEE Transactions on Intelligent Transportation Systems 21, no. 8 (2020): 3525–35. http://dx.doi.org/10.1109/tits.2019.2930731.

Full text
APA, Harvard, Vancouver, ISO, and other styles
21

Brown, Timothy L., John D. Lee, and Daniel V. McGehee. "Human Performance Models and Rear-End Collision Avoidance Algorithms." Human Factors: The Journal of the Human Factors and Ergonomics Society 43, no. 3 (2001): 462–82. http://dx.doi.org/10.1518/001872001775898250.

Full text
APA, Harvard, Vancouver, ISO, and other styles
22

Milanés, Vicente, Joshué Pérez, Jorge Godoy, and Enrique Onieva. "A fuzzy aid rear-end collision warning/avoidance system." Expert Systems with Applications 39, no. 10 (2012): 9097–107. http://dx.doi.org/10.1016/j.eswa.2012.02.054.

Full text
APA, Harvard, Vancouver, ISO, and other styles
23

Wang, Pangwei, WenXiang Wu, Xiaohui Deng, Lin Xiao, Li Wang, and Min Li. "Novel Cooperative Collision Avoidance Model for Connected Vehicles." Transportation Research Record: Journal of the Transportation Research Board 2645, no. 1 (2017): 144–56. http://dx.doi.org/10.3141/2645-16.

Full text
Abstract:
Connected vehicle technology exchanges real-time vehicle and traffic information through vehicle-to-vehicle and vehicle-to-infrastructure communication. The technology has the potential to improve traffic safety applications such as collision avoidance. In this paper, a novel cooperative collision avoidance (CCA) model that could improve the effectiveness of the collision avoidance system of connected vehicles was developed. Unlike traditional collision avoidance models, which relied mainly on emergency braking, the proposed CCA approach avoided collision through a combination of following vehicle deceleration and leading vehicle acceleration. Through spacing policy theory and nonlinear optimization, the model calculated the desired deceleration rate for the following vehicle and the acceleration rate for the leading vehicle, respectively, at each time interval. The CCA approach was then tested on a scaled platform with hardware-in-the-loop simulation embedded with MATLAB/Simulink and a car simulator package, CarSim. Results show that the proposed model can effectively avoid rear-end collisions in a three-vehicle platoon.
APA, Harvard, Vancouver, ISO, and other styles
24

Lai, Fei, and Chaoqun Huang. "Balancing Safety and Comfort: A Novel Automatic Braking Control Method Using Seventh-Degree Polynomials." Algorithms 17, no. 12 (2024): 545. https://doi.org/10.3390/a17120545.

Full text
Abstract:
This study reinterprets the rear-end collision avoidance problem as a trajectory planning challenge, introducing an automatic braking control method based on seventh-degree polynomials. This approach effectively balances vehicle safety and comfort. Unlike traditional automatic braking control methods, e.g., time-to-collision or safety distance models, our method incorporates multiple constraints at both the initiation and conclusion of braking. Consequently, it significantly improves the braking comfort while ensuring collision avoidance; specifically, the braking deceleration changes smoothly rather than abruptly, greatly reducing the vehicle’s jerk value. In accordance with the Euro NCAP testing standards, three car-to-car rear (CCR) test scenarios, such as car-to-car rear stationary (CCRs), car-to-car rear moving (CCRm) and car-to-car rear braking (CCRb), were established within the CarSim environment. The proposed algorithm was rigorously evaluated through integrated simulations performed in CarSim and MATLAB/Simulink, demonstrating its effectiveness.
APA, Harvard, Vancouver, ISO, and other styles
25

Chen, Chen, Hongyun Liu, Hongyu Xiang, Meilian Li, Qingqi Pei, and Shengda Wang. "A Rear-End Collision Avoidance Scheme for Intelligent Transportation System." MATEC Web of Conferences 81 (2016): 02001. http://dx.doi.org/10.1051/matecconf/20168102001.

Full text
APA, Harvard, Vancouver, ISO, and other styles
26

Chen, Keng-Pin, and Pao-Ann Hsiung. "Vehicle Collision Prediction under Reduced Visibility Conditions." Sensors 18, no. 9 (2018): 3026. http://dx.doi.org/10.3390/s18093026.

Full text
Abstract:
Rear-end collisions often cause serious traffic accidents. Conventionally, in intelligent transportation systems (ITS), radar collision warning methods are highly accurate in determining the inter-vehicle distance via detecting the rear-end of a vehicle; however, in poor weather conditions such as fog, rain, or snow, the accuracy is significantly affected. In recent years, the advent of Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I) communication systems has introduced new methods for solving the rear-end collision problem. Nevertheless, there is still much left for improvement. For instance, weather conditions have an impact on human-related factors such as response time. To address the issue of collision detection under low visibility conditions, we propose a Visibility-based Collision Warning System (ViCoWS) design that includes four models for prediction horizon estimation, velocity prediction, headway distance prediction, and rear-end collision warning. Based on the history of velocity data, future velocity volumes are predicted. Then, the prediction horizon (number of future time slots to consider) is estimated corresponding to different weather conditions. ViCoWs can respond in real-time to weather conditions with correct collision avoidance warnings. Experiment results show that the mean absolute percentage error of our velocity prediction model is less than 11%. For non-congested traffic under heavy fog (very low visibility of 120 m), ViCoWS warns a driver by as much as 4.5 s prior to a possible future collision. If the fog is medium with a low visibility of 160 m, ViCoWs can give warnings by about 2.1 s prior to a possible future collision. In contrast, the Forward Collision Probability Index (FCPI) method gives warnings by only about 0.6 s before a future collision. For congested traffic under low visibility conditions, ViCoWS can warn a driver by about 1.9 s prior to a possible future collision. In this case, the FCPI method gives 1.2 s for the driver to react before collision.
APA, Harvard, Vancouver, ISO, and other styles
27

Ahmed Elsayed, Abbas Al-Sadig, Siti Nur Atiqah Halimi, and Mohd Azizi Abdul Rahman. "Image Detection and Distance Estimation for Rear Collision Avoidance using YOLOv4 on Jetson Nano." Journal of Advanced Research in Computing and Applications 32, no. 1 (2024): 22–29. http://dx.doi.org/10.37934/arca.32.1.2229.

Full text
Abstract:
Traffic accidents pose a significant global challenge, resulting in millions of non-fatal injuries annually. Addressing road safety issues and implementing comprehensive measures are crucial. This study evaluates a rear-end pre-collision avoidance system (CAS) utilizing the YOLOv4 algorithm on a small, powerful embedded computer, NVIDIA© Jetson Nano™. The system integrates image detection and distance estimation techniques within a monocular vision framework. Experimental results demonstrate the system's remarkable accuracy in distance estimation. Under normal lighting conditions, it achieved an accuracy rate of 94.60% with a confidence level of 0.778. Even in altered lighting conditions, it maintained a commendable accuracy of 94% with an increased confidence level of 0.8376. Additionally, the system effectively generated warning messages and responded when predefined distance thresholds were reached. These findings highlight the system's practical applicability, particularly in rear collision avoidance scenarios. In a broader context, this research contributes to advancing collision avoidance systems by addressing the critical need for precise distance estimation in rear-end collision scenarios. By enhancing safety and reliability, these systems have the potential to reduce accident risks and elevate road safety significantly. Future research should focus on refining the algorithm, integrating advanced technologies, and conducting extensive tests in diverse environments to optimize further and validate the system's capabilities. These efforts promise to improve road safety and extend the system's applicability in various domains.
APA, Harvard, Vancouver, ISO, and other styles
28

Li, Xiaomeng, Andry Rakotonirainy, Xuedong Yan, and Yuting Zhang. "Driver’s Visual Performance in Rear-End Collision Avoidance Process under the Influence of Cell Phone Use." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 37 (2018): 55–63. http://dx.doi.org/10.1177/0361198118782758.

Full text
Abstract:
Rear-end crash is the most common type of on-road traffic crash, and cell phone use contributes to the increase of rear-end crashes. The effects of cell phone use on driving performance have been thoroughly investigated in previous research with various measurements. However, change in driver’s visual performance while using a cell phone in situations with high rear-end risk has not yet been fully understood. This driving simulator study investigated drivers’ eye movement performance in a rear-end collision avoidance maneuver during cell phone conversation. Eye movement data of 36 participants were collected in a car-following scenario featuring imminent rear-end collision. The whole collision avoidance process was divided into four stages for eye movement data analysis, including normal driving stage, brake response stage, deceleration adjusting stage, and speed recovering stage. Results showed that the average pupil size, fixation duration, and dwell time on the leading vehicle increased significantly during the brake response and deceleration adjusting stages. This indicated that the drivers’ cognitive workload increased during these stages. Drivers used blink inhibition and quick saccade as a visual compensation strategy to mitigate the increased workload from cell phone use during the brake response stage. However, in the deceleration adjusting stage, the cell phone use condition led to a lower fixation frequency on the leading vehicle than in the no phone use condition. Professional drivers were found to pay more visual attention to the leading vehicle than non-professional drivers in the normal driving stage.
APA, Harvard, Vancouver, ISO, and other styles
29

Xue, Yuanfei, and Zhijiang Lou. "Intelligent Control of a Driverless Energy Vehicle Based on an Environment Sensing Sensor." Wireless Communications and Mobile Computing 2022 (May 26, 2022): 1–10. http://dx.doi.org/10.1155/2022/4297888.

Full text
Abstract:
In order to solve the complex environment in the process of vehicle driving and the complexity of self-vehicle structure, intelligent vehicles are prone to rear end collision, lateral collision, and other safety accidents in the presence of tall trees, mountains, and other road environments, endangering the safety of people on board. According to parameters such as the speed of the vehicle, the movement of the blind spot, and the relationship between the vehicle and the blind spot, the model is based on the safety mode of the preceding vehicle. Based on the static obstacles that may exist in the sensing blind area, a sensor sensing blind area safety distance model is established. Based on the possible dynamic obstacles, the active collision avoidance algorithm based on the sensor perceived blind area is studied and simulated. The experimental results show that the selected sensor sensing blind area active collision avoidance controller can well adapt to a variety of special and emergency working conditions, can accurately complete the accurate control of sensor sensing blind area active collision avoidance, and avoid collision accidents to the greatest extent. Compared with the control group, the system designed in this paper can avoid more than 80% of the collision scenes compared with the previous anticollision system. It provides a reference for the future research of sensor sensing blind area-related topics and sensor sensing blind area active collision avoidance system. To a certain extent, it can improve the ability of intelligent vehicle environmental perception and reduce the incidence of rear end collision accidents.
APA, Harvard, Vancouver, ISO, and other styles
30

Mozaffari, Hamed, and Ali Nahvi. "A motivational driver model for the design of a rear-end crash avoidance system." Proceedings of the Institution of Mechanical Engineers, Part I: Journal of Systems and Control Engineering 234, no. 1 (2019): 10–26. http://dx.doi.org/10.1177/0959651819847380.

Full text
Abstract:
A motivational driver model is developed to design a rear-end crash avoidance system. Current driver assistance systems use engineering methods without considering psychological human aspects, which leads to false activation of assistance systems and complicated control algorithms. The presented driver model estimates driver’s psychological motivations using the combined longitudinal and lateral time to collision, the vehicle kinematics, and the vehicle dynamics. These motivations simplify both autonomous driving algorithms and human-machine interactions. The optimal point of a motivational multi-objective cost function defines the decision for the autonomous driving. Moreover, the motivations are used as risk assessment factors for driver–machine interaction in dangerous situations. The system is evaluated on 10 human subjects in a driving simulator. The assistance system had no false activation during the tests. It avoided collisions in all the rear-end crash avoidance scenarios, while 90% of human subjects did not.
APA, Harvard, Vancouver, ISO, and other styles
31

Zhao, Ying, Haijun Li, Yan Huang, and Junyu Hang. "Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles." Sensors 24, no. 1 (2023): 137. http://dx.doi.org/10.3390/s24010137.

Full text
Abstract:
The rapid growth in the number of electric bicycles (e-bicycles) has greatly improved daily commuting for residents, but it has also increased traffic collisions involving e-bicycles. This study aims to develop an autonomous emergency braking (AEB) system for e-bicycles to reduce rear-end collisions. A framework for the AEB system composed of the risk recognition function and collision avoidance function was designed, and an e-bicycle following model was established. Then, numerical simulations were conducted in multiple scenarios to evaluate the effectiveness of the AEB system under different riding conditions. The results showed that the probability and severity of rear-end collisions involving e-bicycles significantly decreased with the application of the AEB system, and the number of rear-end collisions resulted in a 68.0% reduction. To more effectively prevent rear-end collisions, a low control delay (delay time) and suitable risk judgment criteria (TTC threshold) for the AEB system were required. The study findings suggested that when a delay time was less than or equal to 0.1 s and the TTC threshold was set at 2 s, rear-end collisions could be more effectively prevented while minimizing false alarms in the AEB system. Additionally, as the deceleration rate increased from 1.5 m/s2 to 4.5 m/s2, the probability and average severity of rear-end collisions also increased by 196.5% and 42.9%, respectively. This study can provide theoretical implications for the design of the AEB system for e-bicycles. The established e-bicycle following model serves as a reference for the microscopic simulation of e-bicycles.
APA, Harvard, Vancouver, ISO, and other styles
32

Regan, D., S. Hamstra, and S. Kaushal. "Visual Factors in the Avoidance of Front-To-Rear-End Highway Collisions." Proceedings of the Human Factors Society Annual Meeting 36, no. 13 (1992): 1006–10. http://dx.doi.org/10.1177/154193129203601319.

Full text
Abstract:
Two visual factors in the avoidance of front-to-rear-end collisions are (a) judging time to collision so as to control braking optimally on a moment-to-moment basis, and/or (b) judging one's heading relative to the lead car so as to steer appropriately. It is known that time to contact equals θ/(dθ/dt) and it is also known that the eye is sensitive to θ and, separately, (dθ/dt) (θ is the angular size and (dθ/dt) is the rate of increase of angular size). But whether the eye is sensitive to the ratio (θ/(dθ/dt) and, if so, whether drivers use this information are further questions. We report here that the human visual system does contain neurons sensitive to the ratio θ/(dθ/dt) rather independently of θ and (dθ/dt). It is important that the driver looks directly at the lead vehicle: sensitivity to (dθ/dt) falls off steeply in peripheral view. But, over a wide range, sensitivity to (dθ/dt) is independent of contrast. In addition to the classical disparity-driven system for binocular depth perception, there is a separate binocular system for motion in depth. Precise judgements (0.2 deg) of heading are supported by this stereomotion system, but on the other hand about 20% of the population have stereomotion “blind spots” (i.e. field defects). Monocularly-available informations can also support precise judgements of heading, and field defects seem to be rare. Field studies on flight simulators and telemetry-tracked jet aircraft showed that laboratory measures of sensitivity to (dθ/dt) and to the rate of expansion of the optical flow field predicted intersubject differences in performance on flying tasks that were closely related to the rear-end collision situation.
APA, Harvard, Vancouver, ISO, and other styles
33

Abdulhamid, Mohanad, and Otieno Amondi. "Collision Avoidance System Using Ultrasonic Sensor." Land Forces Academy Review 25, no. 3 (2020): 259–66. http://dx.doi.org/10.2478/raft-2020-0031.

Full text
Abstract:
AbstractThis paper describes automobile collision avoidance system by using of an ultrasonic sensor for a vehicle. We utilize the electronic systems application embedded in car that is anticipated to minimize the disaster of car accident. This paper is concentrating on developing a model of rear end car collision avoidance system that detects the gap among motors moving in the identical lane, inside the identical direction and alert the driver each time she or he is in danger range by using a microcontroller. The gap is measured via an ultrasonic sensor used to experience the obstacle beforehand.
APA, Harvard, Vancouver, ISO, and other styles
34

Manjunath, K. G., and N. Jaisankar. "Simulink implementation of IEEE 802.11p for rear end collision avoidance system." International Journal of Convergence Computing 1, no. 3/4 (2015): 232. http://dx.doi.org/10.1504/ijconvc.2015.076031.

Full text
APA, Harvard, Vancouver, ISO, and other styles
35

Shah, Jitendra, Matt Best, Ahmed Benmimoun, and Mohsen Lakehal Ayat. "Autonomous rear-end collision avoidance using an electric power steering system." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 229, no. 12 (2015): 1638–55. http://dx.doi.org/10.1177/0954407014567517.

Full text
APA, Harvard, Vancouver, ISO, and other styles
36

Benedetto, Francesco, Alessandro Calvi, Fabrizio D’Amico, and Gaetano Giunta. "Applying telecommunications methodology to road safety for rear-end collision avoidance." Transportation Research Part C: Emerging Technologies 50 (January 2015): 150–59. http://dx.doi.org/10.1016/j.trc.2014.07.008.

Full text
APA, Harvard, Vancouver, ISO, and other styles
37

Chen, Maolin, Xingqun Zhan, Xin Zhang, and Weichuan Pan. "Localisation-based autonomous vehicle rear-end collision avoidance by emergency steering." IET Intelligent Transport Systems 13, no. 7 (2019): 1078–87. http://dx.doi.org/10.1049/iet-its.2018.5348.

Full text
APA, Harvard, Vancouver, ISO, and other styles
38

McGehee, Daniel V., Thomas A. Dingus, and Avraham D. Horowitz. "The Potential Value of a Front-to-Rear-End Collision Warning System Based on Factors of Driver Behavior, Visual Perception and Brake Reaction Time." Proceedings of the Human Factors Society Annual Meeting 36, no. 13 (1992): 1003–5. http://dx.doi.org/10.1177/154193129203601318.

Full text
Abstract:
The potential value of a front-to-rear-end collision warning system based on factors of driver behavior, visual perception and brake reaction time is examined in this paper. Twenty-four percent of all motor vehicle crashes involving two or more vehicles are front-to-rear-end collisions. These collisions demonstrate that several driver performance factors are common. The literature indicates that drivers use the relative size and the visual angle of the vehicle ahead when making judgments regarding depth. In addition, drivers often have difficulty gauging velocity differences and depth cues between themselves and the vehicle they are following. Finally, drivers often follow at distances that are closer than brake-reaction time permits for accident avoidance. It is apparent that the comfort level of close following behavior increases over time due to the rarity of consequences. Experience also teaches drivers that the vehicle in front does not suddenly slow down very often. On the basis of these driver behavior and human performance issues, a front-to-rear-end collision warning system that provides headway/following distance and velocity change information is considered. Based on the driver performance issues, display design recommendations are outlined. The value of such a device may be demonstrated by the added driver safety and situation awareness provided. The long-term goal would ultimately be the reduction of one of the most frequent type of automobile crashes.
APA, Harvard, Vancouver, ISO, and other styles
39

Hou, Yew Cheong, Khairul Salleh Mohamed Sahari, Leong Yeng Weng, et al. "Development of collision avoidance system for multiple autonomous mobile robots." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142092396. http://dx.doi.org/10.1177/1729881420923967.

Full text
Abstract:
This article presents a collision avoidance system for multiple robots based on the current autonomous car collision avoidance system. The purpose of the system is to improve the current autonomous car collision avoidance system by including data input of other vehicles’ velocity and positioning via vehicle-to-vehicle communication into the current autonomous car collision avoidance system. There are two TurtleBots used in experimental testing. TurtleBot is used as the robot agent while Google Lightweight Communication and Marshalling is used for inter-robot communication. Additionally, Gazebo software is used to run the simulation. There are two types of collision avoidance system algorithm (collision avoidance system without inter-robot communication and collision avoidance system with inter-robot communication) that are developed and tested in two main road crash scenarios, rear end collision scenario and junction crossing intersection collision scenario. Both algorithms are tested and run both in simulation and experiment setup, each with 10 repetitions for Lead TurtleBot sudden stop, Lead TurtleBot decelerate, Lead TurtleBot slower speed, and straight crossing path conditions. Simulation and experimental results data for each algorithm are recorded and tabulated. A comprehensive comparison of performance between the proposed algorithms is analyzed. The results showed that the proposed system is able to prevent collision between vehicles with an acceptable success rate.
APA, Harvard, Vancouver, ISO, and other styles
40

Xue, Qingwan, Xijun Ouyang, Yi Zhao, and Weiwei Guo. "Effect of Situation Kinematics on Drivers’ Rear-End Collision Avoidance Behaviour—A Combined Effect of Visual Looming, Speed, and Distance Analysis." Sustainability 14, no. 22 (2022): 15103. http://dx.doi.org/10.3390/su142215103.

Full text
Abstract:
Considering the large proportion of rear-end collisions occurring in our daily life and the severity it may lead to, the objective of this study was to investigate the effect of situation kinematics on drivers’ rear-end collision avoidance behaviour after brake onset. A wide range of lead vehicle deceleration scenarios were designed based on driving simulator experiments to collect drivers’ deceleration behaviour data. Different from measures (e.g., speed, the lead vehicle’s deceleration et al.) often adopted in previous studies, a visual looming-based measure at different time points was calculated combined with analysis of speed and distance to quantify situation kinematics in this study. The Spearman’s nonparametric rank correlation test was firstly conducted to examine the correlation between visual looming-based metrics and related deceleration behaviour. The mixed model was performed on drivers’ brake jerk and maximum deceleration rate, while the logistic model was then performed to predict the probability of the occurrence of rear-end collisions. Spearman’s nonparametric test showed that both deceleration ramp-up and drivers’ maximum deceleration rate increase significantly as the looming traces increase faster. Results of the logistic model indicated that the probability of occurrence of a potential collision might be higher if the situation at the brake onset is quite urgent and braking is moderate. It was demonstrated that both drivers’ deceleration ramp-up and maximum deceleration rate could be highly kinematic-dependent, and visual looming, driving speed, and distance can be useful information for drivers to take relative deceleration actions.
APA, Harvard, Vancouver, ISO, and other styles
41

Verma, Rajat, Ramin Saedi, Ali Zockaie, and Timothy J. Gates. "Behavioral Analysis of Drivers Following Winter Maintenance Trucks Enabled with Collision Avoidance System." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 10 (2019): 394–404. http://dx.doi.org/10.1177/0361198119850131.

Full text
Abstract:
Winter maintenance trucks (WMTs) often operate at lower speeds during inclement weather and roadway conditions, creating potential safety issues for motorists following close behind. In this study, a new prototype radar-based rear-end collision avoidance and mitigation system (CAMS) was tested to assess its impact on the behavior of drivers following WMTs. The system is designed to flash an auxiliary rear-facing warning light upon detection of a vehicle encroaching within an unsafe relative headway with the rear of the WMT. A series of field evaluations was performed during actual winter maintenance operations to assess the effectiveness of the system compared with normal operating conditions (i.e., without the CAMS warning light) toward improving driver behavior related to rear-end crash risk. Specifically, two measures were assessed: (a) rate of vehicles encroaching beyond a safe time headway threshold to the rear of the WMT, and (b) the reaction–response time of drivers. Classification and regression tree models were created for identifying the relevant factors influential in determining the change in driver response. The results indicate that this warning light was effective in reducing the likelihood of the subject drivers crossing beyond a relative headway of 4.5 s. It was also effective in reducing the reaction and response times of the drivers by 0.83 and 0.55 s (36% and 20% reduction), respectively. Although the results were encouraging, additional field testing is recommended before conclusions are drawn regarding the traffic safety impacts of the system.
APA, Harvard, Vancouver, ISO, and other styles
42

Wang, Xuesong, Meixin Zhu, Ming Chen, and Paul Tremont. "Drivers’ rear end collision avoidance behaviors under different levels of situational urgency." Transportation Research Part C: Emerging Technologies 71 (October 2016): 419–33. http://dx.doi.org/10.1016/j.trc.2016.08.014.

Full text
APA, Harvard, Vancouver, ISO, and other styles
43

An, Natalya, Jens Mittag, and Hannes Hartenstein. "Designing fail-safe and traffic efficient 802.11p-based rear-end collision avoidance." Ad Hoc Networks 37 (February 2016): 3–13. http://dx.doi.org/10.1016/j.adhoc.2015.08.006.

Full text
APA, Harvard, Vancouver, ISO, and other styles
44

Doi, A. "Development of a rear-end collision avoidance system with automatic brake control." JSAE Review 15, no. 4 (1994): 335–40. http://dx.doi.org/10.1016/0389-4304(94)90216-x.

Full text
APA, Harvard, Vancouver, ISO, and other styles
45

Sun, Zhong Liang, Xiao Kan Wang, and Shou Xiang Zai. "Research on Collision Avoidance Method of Car Anti-Head-and-Rear Based on Safe Distance Model." Advanced Materials Research 243-249 (May 2011): 4435–40. http://dx.doi.org/10.4028/www.scientific.net/amr.243-249.4435.

Full text
Abstract:
The present rear-end collision accident proportion on the road increases day after day, car collision avoidance system is more and more paid attention. Analysising the existing car collision avoidance system, we propose a car anti-collision algorithm based on safe distance model in this paper. This method takes the influence factors of safety distance for main parameters which fully considers the speed change and the acceleration change of the car 1 and the car 2. It may realize real-time information acquisition and warning judgment according to the state of car 2, the car could automatic braking if necessary. VB simulation shows that the algorithm can effectively avoid collision, also automatically maintain the distance between vehicles, and lay a foundation for further research on the unmanned car.
APA, Harvard, Vancouver, ISO, and other styles
46

Wu, Xingwei, Linda Ng Boyle, and Dawn Marshall. "Drivers’ Avoidance Strategies When Using a Forward Collision Warning (FCW) System." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 61, no. 1 (2017): 1939–43. http://dx.doi.org/10.1177/1541931213601964.

Full text
Abstract:
Forward collision warning (FCW) systems help prevent rear-end collisions by identifying and alerting drivers of threats ahead. Understanding drivers’ avoidance strategies i.e. the tendency to brake or steer is important for the design and effectiveness of these systems. A driving simulator study was performed across five US locations to examine three driver avoidance maneuvers: braking only, steering only and combined braking and steering. A log-linear analysis was used to investigate the likelihood of an avoidance maneuver given the driver characteristics (age, gender) and study location. Findings showed that drivers aged 40 years and older were more likely to use a combined braking and steering maneuver to avoid a rearend collision. Drivers from two coastal urban areas (Washington, D.C. and Seattle, WA) were less likely to choose braking only in response to FCW alerts. Younger drivers and drivers that live in more rural areas (Clemson, SC and Iowa City, IA) were more likely to select braking only to avoid a crash, which could be due to their experience in less congested traffic environment. The findings of this study provide some insights on the factors associated with various avoidance strategies among drivers. This understanding can help guide the design of future in-vehicle collision warning systems.
APA, Harvard, Vancouver, ISO, and other styles
47

Yin, Xiao Qin, and Ming Xia Wang. "Safety Distance Mathematical Model of Pro-Active Head Restraint Based on Fuzzy Theory." Applied Mechanics and Materials 687-691 (November 2014): 710–14. http://dx.doi.org/10.4028/www.scientific.net/amm.687-691.710.

Full text
Abstract:
Pro-active head restraint is a new automotive safety device. It can make a pre-estimation of the occurrence possibility of rear-end collision. Thus, the whiplash injury can be effectively reduced or even prevented. One of the key elements in rear-end collision avoidance system is to establish an effective safety distance mathematical model. Based on the running state of front car, the related calculation models of safety distance are established by means of dynamical and kinematical analysis of vehicle braking process and following process. Taken randomness of parameters into consideration, the fuzzy relations between these parameters should be validated by means of fuzzy theory. By using the MATLAB software, the study shows that safety distance model and the methods used to determine parameters are reasonable, and the false alarm can be effectively minimized.
APA, Harvard, Vancouver, ISO, and other styles
48

Son, Young Seop, and Wonhee Kim. "Cooperation-Based Risk Assessment Prediction for Rear-End Collision Avoidance in Autonomous Lane Change Maneuvers." Actuators 11, no. 4 (2022): 98. http://dx.doi.org/10.3390/act11040098.

Full text
Abstract:
In this study, we present an innovative approach to risk assessment for rear-end collision avoidance using a cooperation concept for an autonomous lane change system. A Kalman filter is designed to estimate the longitudinal acceleration and predict the relative longitudinal position, velocity, and acceleration. Risk assessment is performed using the predicted motion of the object vehicle in the target lane. The cooperation concept is proposed to improve the flexibility of the lane change. If the risk assessment for the lane change indicates collision risk, the cooperativeness of the driver of the object vehicle is determined. If the driver of the object vehicle is regarded as a cooperative driver, within the original lane, the ego vehicle moves toward the target lane in preparation for the lane change. Subsequently, as soon as the risk assessment indicates that there is no collision risk, the lane change is performed. Thus, unlike conventional methods, the autonomous lane change using the proposed risk assessment can be initiated. Furthermore, the proposed risk assessment using cooperation concept is more flexible compared with previous methods for autonomous lane change in cluttered traffic.
APA, Harvard, Vancouver, ISO, and other styles
49

Lee, Donghoun, Sehyun Tak, Seongjin Choi, and Hwasoo Yeo. "Development of Risk Predictive Collision Avoidance System and Its Impact on Traffic and Vehicular Safety." Transportation Research Record: Journal of the Transportation Research Board 2673, no. 7 (2019): 454–65. http://dx.doi.org/10.1177/0361198119836972.

Full text
Abstract:
Various collision avoidance systems (CASs) have been developed and employed in human-operated vehicles as well as more recently in autonomous vehicles. Most of the existing CASs perform an override function to actuate automatic emergency braking in a critical situation based on the current traffic information obtained from in-vehicle sensors or short-range vehicular communications. These CASs focus on the critical situation in the vicinity of the subject vehicle, which means they may have negative influences on the subject vehicle and its following vehicles, particularly when the leader vehicle of a platoon with short headway applies harsh braking to mitigate an impending collision risk. This study proposes a risk predictive CAS (RPCAS) which executes predictive deceleration with mild braking in advance to prevent a potential rear-end collision by predicting the collision risk arising from a downstream site. To evaluate the performance of the RPCAS, the proposed system is compared with several existing CASs in various car-following cases based on a microscopic traffic simulation. The simulation results show that the RPCAS can effectively reduce the rear-end collision risk with less harsh braking compared with the existing CASs. Furthermore, the RPCAS enables vehicles arriving from upstream to anticipate a potential crash, which provides them with sufficient time to reduce their current speeds proactively. The research findings suggest that the proposed system can attenuate the negative impacts of the previous CASs in relation to traffic and vehicular safety.
APA, Harvard, Vancouver, ISO, and other styles
50

Rajaram, Vignesh, and Shankar C. Subramanian. "A model-based rear-end collision avoidance algorithm for heavy commercial road vehicles." Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering 229, no. 5 (2014): 550–62. http://dx.doi.org/10.1177/0954407014547243.

Full text
APA, Harvard, Vancouver, ISO, and other styles
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography