Relevant bibliographies by topics / Rear-end collision avoidance

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Rear-end collision avoidance'

Author: Grafiati
Published: 4 June 2025
Last updated: 23 June 2025

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

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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.
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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.

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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.

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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.

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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.
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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.

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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.
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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.

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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.
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Dissertations / Theses on the topic "Rear-end collision avoidance"

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Dravidam, Uttamkumar. "Development of rear-end collision avoidance in automobiles." FIU Digital Commons, 1999. http://digitalcommons.fiu.edu/etd/3084.

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The goal of this work is to develop a Rear-End Collision Avoidance System for automobiles. In order to develop the Rear-end Collision Avoidance System, it is stated that the most important difference from the old practice is the fact that new design approach attempts to completely avoid collision instead of minimizing the damage by over-designing cars. Rear-end collisions are the third highest cause of multiple vehicle fatalities in the U.S. Their cause seems to be a result of poor driver awareness and communication. For example, car brake lights illuminate exactly the same whether the car is slowing, stopping or the driver is simply resting his foot on the pedal. In the development of Rear-End Collision Avoidance System (RECAS), a thorough review of hardware, software, driver/human factors, and current rear-end collision avoidance systems are included. Key sensor technologies are identified and reviewed in an attempt to ease the design effort. The characteristics and capabilities of alternative and emerging sensor technologies are also described and their performance compared. In designing a RECAS the first component is to monitor the distance and speed of the car ahead. If an unsafe condition is detected a warning is issued and the vehicle is decelerated (if necessary). The second component in the design effort utilizes the illumination of independent segments of brake lights corresponding to the stopping condition of the car. This communicates the stopping intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions. In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor.
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Schreiner, Lisa Marie. "An Investigation of the Effectiveness of A Strobe Light As An Imminent Rear Warning Signal." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/35887.

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Strobe lights have been used successfully in many transportation applications to increase conspicuity. It was hoped that a strobe signal could also be applied to more effectively warn distracted drivers of an unexpected rear end conflict. This "proof of concept study" used a 2 x 2 between-subjects design using thirty-three subjects (16 subjects in the strobe condition, 17 subjects in the no strobe condition) who were divided into two age groups: younger (25-35) and older (60-70). The driver unexpectedly encountered a stopped "surrogate" vehicle in the roadway (with or without a rear-facing strobe light) in a controlled on-road study at the Smart Road located at the Virginia Tech Transportation Institute (VTTI). Results suggested that younger subjects' perception times improved as a result of being exposed to the strobe signal. Faster perception of the situation allowed more time to initiate a brake response. Older subjects perception and response times remained unchanged by the strobe signal. More severe initial steering rate and subjective responses indicated that the strobe conveyed a sense of urgency irrespective of age. Visual distraction of subjects proved difficult. Hence, the impact of the strobe on attracting the attention of a visually distracted driver to the stimulus could not be as fully investigated as originally hoped. The formulation of a more difficult distraction task was suggested for future research to truly assess the ability of the strobe light at alerting visually distracted drivers.<br>Master of Science
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McLaughlin, Shane Brendan. "Analytic Assessment of Collision Avoidance Systems and Driver Dynamic Performance in Rear-End Crashes and Near-Crashes." Diss., Virginia Tech, 2007. http://hdl.handle.net/10919/29561.

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Collision avoidance systems (CASs) are being developed and fielded to reduce the number and severity of rear-end crashes. Kinematic algorithms within CASs evaluate sensor input and apply assumptions describing human-response timing and deceleration to determine when an alert should be presented. This dissertation presents an analytic assessment of dynamic function and performance CASs and associated driver performance for preventing automotive rear-end crashes. A method for using naturalistic data in the evaluation of CAS algorithms is described and applied to three algorithms. Time-series parametric data collected during 13 rear-end crashes and 70 near-crashes are input into models of collision avoidance algorithms to determine when the alerts would have occurred. Algorithm performance is measured by estimating how much of the driving population would be able to respond in the time available between when an alert would occur and when braking was needed. A sensitivity analysis was performed to consider the effect of alternative inputs into the assessment method. The algorithms were found to warn in sufficient time to permit 50â 70% of the population to avoid collision in similar scenarios. However, the accuracy of this estimate was limited because the tested algorithms were found to alert too frequently to be feasible. The response of the assessment method was most sensitive to differences in assumed response-time distributions and assumed driver braking levels. Low-speed crashes were not addressed by two of the algorithms. Analysis of the events revealed that the necessary avoidance deceleration based on kinematics was generally less than 2 s in duration. At the time of driver response, the time remaining to avoid collision using a 0.5g average deceleration ranged from â 1.1 s to 2.1 s. In 10 of 13 crashes, no driver response deceleration was present. Mean deceleration for the 70 near-crashes was 0.37g and maximum was 0.72g. A set of the events was developed to measure driver response time. The mean driver response time was 0.7 s to begin braking and 1.1 s to reach maximum deceleration. Implications for collision countermeasures are considered, response-time results are compared to previous distributions and future work is discussed.<br>Ph. D.
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Kim, Yong-Seok. "Effects of Driver, Vehicle, and Environment Characteristics on Collision Warning System Design." Thesis, Linköping University, Department of Science and Technology, 2001. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-1121.

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<p>The purpose of the present study was to examine effects of driver, vehicle, and environment characteristics on Collision Warning System (CWS) design. One hypothesis was made that the capability of collision avoidance would not be same among a driver, vehicle, and environment group with different characteristics. Accident analysis and quantitative analysis was used to examine this hypothesis in terms of ‘risk’ and ‘safety margin’ respectively. Rear-end collision had a stronger focus in the present study. </p><p>As a result of accident analysis, heavy truck showed a higher susceptibility of the fatal rear-end accidents than car and light truck. Also, dry road surface compared to wet or snow, dark condition compared to daylight condition, straight road compared to curved road, level road compared to grade, crest or sag, roadway having more than 5 travel lanes compared to roadway having 2, 3 or 4 travel lanes showed a higher susceptibility of the fatal rear-end accidents. Relative rear-end accidents involvement proportion compared to the other types of collision was used as a measure of susceptibility. </p><p>As a result of quantitative analysis, a significant difference in terms of Required Minimum Warning Distance (RMWD) was made among a different vehicle type and braking system group. However, relatively small difference was made among a different age, gender group in terms of RMWD. Based on the result, breaking performance of vehicle should be regarded as an input variable in the design of CWS, specifically warning timing criteria, was concluded.</p>
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Chou, Chen-Ju, and 周辰儒. "Research on the rear-end collision avoidance and warning system by rear-end camera." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/10187206141637199024.

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碩士<br>國立臺灣大學<br>土木工程學研究所<br>96<br>The objective of this study is to construct the rear-end collision avoidance and warning system of advance safety vehicle (ASV) by rear-end parking camera. Based on image processing technology to get and analyze the driving environmental data, we developed the rear-end collision warning logic and system. Since the automobile electronics industry developed faster, the parking assist system, like the ultrasonic sensors and rear-end camera, almost become the basic equipment in vehicles. However, the parking assist equipments are only turned on when in parking mode. Therefore, to provide the following vehicle information for subjective one, this study tends to apply the rear-end camera in vehicle when moving forward. It not only reduces the equipment cost of the vehicle but also increases the safety for drivers. The main idea of warning system is to prevent accidents which caused by inattentive of drivers. This study used the image processing to detect the longitudinal data of the following vehicle, including the relative distance, velocity, and acceleration. We developed the dynamic thresholds by the relative data and the drivers’ perceptive reaction time to issue the warning signal for drivers. The α-β-γ filter was applied in this section to get the smoother relative data. This study built up a rear-end collision avoidance and warning system by rear-end camera. We constructed the warning program by Borland C++ Builder. The warning hardware of the system was used the industry personal computer and on-board video equipment. The experiment result was successful for the off-line video test. In other words, this study proves that the developed rear-end warning system could appropriately issue the warning signal for drivers.
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Chou, Chen-Ju. "Research on the rear-end collision avoidance and warning system by rear-end camera." 2008. http://www.cetd.com.tw/ec/thesisdetail.aspx?etdun=U0001-3006200811031800.

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Chou, Ying-Ru, and 周盈如. "Development and Application of Parameters Fuzzification for The Bus Rear-End Collision Avoidance Warning Algorithm." Thesis, 2005. http://ndltd.ncl.edu.tw/handle/01628717565486522816.

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碩士<br>中華大學<br>科技管理研究所<br>93<br>Vehicle rear-end collision avoidance warning system (RCAWS) or forward collision avoidance warning system (FCAWS) is the system that integrates the advanced detecting technology, auditory, visual or tactile display devices, and rear end collision warning algorithm to provide the timely alert messages to drivers according to different road and traffic conditions. The alert messages can be used to warn drivers to keep safety spacing between the lead vehicle and the following vehicle for avoiding rear end crashes. The effect of safety protection will also be achieved. Since the frequency of bus accidents due to without keeping safety spacing is very high in Taiwan, it has become an important issue of advanced safety bus technology research to develop the bus rear-end collision avoidance warning system concerns bus driver’s driving characteristics. While the rear-end collision avoidance warning system offers great potential to improve automobile safety, beneficial effects depend on the joint performance of the system and the driver psychology and behavior acceptance. By reviewing the developed and developing RCAWS algorithms, driver’s perception reaction time, braking deceleration and stationary vehicle spacing of the warning threshold are three major parameters in the RCAWS algorithm. These parameters influence the timing of warning system opening. The proper combination of these three parameters will be different by different driver psychology-behavior characteristics. This study designed the emergency braking simulation scenario of bus car following driving on the freeway straight road section by utilizing the bus driving simulator. The bus drivers with license that are working in freeway bus companies were invited to do the simulation experiments under this designed driving simulation scenario. The sample data of perception reaction time, braking deceleration and stationary vehicle spacing were collected and analyzed after bus driving simulator experiments. The value range of perception reaction time is from 0.72 seconds to 3.23 seconds. The value range of braking deceleration is from -1.47 meters/square second to -7.25 meters/square second. The value range of stationary vehicle spacing is from 2 meters to 12 meters. This study developed the safety membership function of the three parameters and analyzed the reasonable bus rear-end collision avoidance algorithms with related warning rules. In first algorithm, twenty-seven safety levels of warning distance equation and the related rules were also developed through the analysis of fuzzy operation rules and defuzzification methods. In second algorithm, the -cut was developed to set up reasonable fuzzy warning equation according to different driver characteristics. The results of this study will be a useful basis in developing the rear-end collision avoidance warning system of advanced safety bus.
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Books on the topic "Rear-end collision avoidance"

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Rhodes, Allen D. Forward Collision Avoidance Systems and the Prevention of Rear-End Collisions. Nova Science Publishers, Incorporated, 2015.

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Book chapters on the topic "Rear-end collision avoidance"

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Nekovee, Maziar, and Jing Bie. "Rear-End Collision: Causes and Avoidance Techniques." In Wireless Vehicular Networks for Car Collision Avoidance. Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4419-9563-6_4.

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M. S., Abirami, and Manoj Kushwaha. "Minimizing Data Loss by Encrypting Brake-Light Images and Avoiding Rear-End Collisions Using Artificial Neural Network." In Innovative Machine Learning Applications for Cryptography. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1642-9.ch008.

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Rear-end collisions are a threat to road safety, so reliable collision avoidance technologies are essential. Traditional systems present several issues due to data loss and privacy concerns. The authors introduce an encrypted artificial neural network (ANN) method to prevent front-vehicle rear-end collisions. This system uses encryption techniques and ANN algorithm to recognize the front vehicle brake light in real time. Information can't be deciphered without the appropriate key using encryption. Intercepting data during transmission prevents reading. The system works day and night. ANN outperforms LR, SVM, DT, RF, and KNN in accuracy. An encrypted ANN-based ML model distinguishes between brake and normal signals. ANN accuracy was 93.7%. Driver receives further alerts to avoid rear-end collisions. This work proposes a lightweight, secure ANN-based brake light picture encryption method. The proposed approach may be applied to other collision circumstances, including side and frontal strikes. The technique would be more adaptable and applicable to many road safety circumstances.
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Conference papers on the topic "Rear-end collision avoidance"

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Perera, Ranuja, Surith Arawwala, Shalika Harshamali, et al. "Rear-End Collision Avoidance Using Multi-Agent Deep Reinforcement Learning." In 2024 6th International Conference on Advancements in Computing (ICAC). IEEE, 2024. https://doi.org/10.1109/icac64487.2024.10851036.

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Uc, Ergun Y., Matthew Rizzo, Steve W. Anderson, Qian Shi, and Jeffrey D. Dawson. "Unsafe Rear-End Collision Avoidance in Alzheimer's Disease." In Driving Assessment Conference. University of Iowa, 2005. http://dx.doi.org/10.17077/drivingassessment.1170.

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Liang Li, Guangquan Lu, Yunpeng Wang, and Daxin Tian. "A rear-end collision avoidance system of connected vehicles." In 2014 IEEE 17th International Conference on Intelligent Transportation Systems (ITSC). IEEE, 2014. http://dx.doi.org/10.1109/itsc.2014.6957667.

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Basjaruddin, Noor Cholis, Zakka Izzatur Rahman Noor, and Dwi Hendratmo Widyantoro. "Multi Agent Protocol for Cooperative Rear-end Collision Avoidance System." In 2019 2nd International Conference on Applied Information Technology and Innovation (ICAITI). IEEE, 2019. http://dx.doi.org/10.1109/icaiti48442.2019.8982117.

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Lyu, Feng, Hongzi Zhu, Nan Cheng, et al. "ABC: Adaptive Beacon Control for Rear-End Collision Avoidance in VANETs." In 2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON). IEEE, 2018. http://dx.doi.org/10.1109/sahcn.2018.8397130.

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Shah, Jitendra, and Mohamed Benmimoun. "Driver Perceived Threat and Behavior in Rear End Collision Avoidance Situations." In SAE 2015 World Congress & Exhibition. SAE International, 2015. http://dx.doi.org/10.4271/2015-01-1414.

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Sancar, Feyyaz Emre, Baris Fidan, Jan P. Huissoon, and Steven L. Waslander. "MPC based collaborative adaptive cruise control with rear end collision avoidance." In 2014 IEEE Intelligent Vehicles Symposium (IV). IEEE, 2014. http://dx.doi.org/10.1109/ivs.2014.6856559.

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Shiqing, Ding, Song Yandong, and Ding Jibin. "The Research for Mechanism of Vehicle Rear-End Collision Avoidance System." In 2010 International Conference on Intelligent Computation Technology and Automation (ICICTA). IEEE, 2010. http://dx.doi.org/10.1109/icicta.2010.381.

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Ye, F., M. Adams, and S. Roy. "V2V Wireless Communication Protocol for Rear-End Collision Avoidance on Highways." In ICC 2008 - 2008 IEEE International Conference on Communications Workshops. IEEE, 2008. http://dx.doi.org/10.1109/iccw.2008.77.

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An, Natalya, Jens Mittag, and Hannes Hartenstein. "Designing fail-safe and traffic efficient 802.11p-based rear-end collision avoidance." In 2014 IEEE Vehicular Networking Conference (VNC). IEEE, 2014. http://dx.doi.org/10.1109/vnc.2014.7013303.

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