A review of essential technologies for collision avoidance assistance systems

Zhao, Zhiguo; Zhou, Lei; Zhu, Qiang; Luo, Yugong; Li, Keqiang

doi:10.1177/1687814017725246

Cited by 17 publications

(6 citation statements)

References 83 publications

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“…Finally, the simulation results showed that the intelligent right-turning vehicle collision probability calculation algorithm could calculate collision probability and the two- Scenario 2: when the vehicle turned right into the lane, it was difficult to find pedestrians who were crossing the road in the lane, so it was easy to cause serious traffic accidents. e parameters in this scenario were defined as follows: pt � 0.01 s, T � 5 s, L 1 � 8 m, W 1 � 2 m, and D � 1.5 m; the pedestrian's coordinates were (8,12). V 1 , V 2 , and R consist of 10,000 normally distributed random numbers, N (1, 2), and R ∼ N (20, 1); V 2 represented the velocity of the pedestrian in this case.…”

Section: Simulation Results Analysis Of Four Modesmentioning

confidence: 99%

“…In rightturning collision-related research, Sitao et al put forward an intersection optimization design to reduce the collision probability between right-turning vehicles and pedestrians [11], but it cannot cover all possible right-turning collisions in intersections. Zhao et al have conducted research in intelligent vehicles active collision avoidance related fields [12]. Choi and Zhao et al adopted the autonomous emergency braking (AEB) system to avoid collisions [13,14].…”

Section: Introductionmentioning

confidence: 99%

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Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Shen

Shan

Gao

et al. 2020

Journal of Advanced Transportation

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With the development of intelligent vehicle technology, the demand for advanced driver assistant systems kept increasing. To improve the performance of the active safety systems, we focused on right-turning vehicle’s collision warning and avoidance. We put forward an algorithm based on Monte Carlo simulation to calculate the collision probability between the right-turning vehicle and another vehicle (or pedestrian) in intersections. We drew collision probability curves which used time-to-collision as the horizontal axis and collision probability as the vertical axis. We established a three-level collision warning system and used software to calculate and simulate the collision probability and warning process. To avoid the collision actively when turning right, a two-stage braking strategy is applied. Taking four right-turning collision conditions as examples, the two-stage braking strategy was applied, analysing and comparing the anteroposterior curve diagram simultaneously to avoid collision actively and reduce collision probability. By comparison, the collision probability 2 s before active collision avoidance was more than 80% and the collision probability may even reach 100% in certain conditions. To improve the active safety performance, the two-stage braking strategy can reduce the collision probability from exceeding 50% to approaching 0% in 2 s and reduce collision probability to less than 5% in 3 s. By changing four initial positions, the collision probability curve calculation algorithm and the two-stage braking strategy are validated and analysed. The results verified the rationality of the collision probability curve calculation algorithm and the two-stage braking strategy.

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Section: Simulation Results Analysis Of Four Modesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Shen

Shan

Gao

et al. 2020

Journal of Advanced Transportation

View full text Add to dashboard Cite

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“…However, their implementation is a major challenge for both rule-based control and data-driven decision-making. Readers are encouraged to refer to 31,32 for the appropriate analysis of key technologies for assistance systems for collision prevention. Multiple business giants in different countries are currently working on the production of AVs.…”

Section: Overview Of Mvccamentioning

confidence: 99%

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

Muzahid

Kamarulzaman

Rahman

et al. 2023

Sci Rep

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Prospective customers are becoming more concerned about safety and comfort as the automobile industry swings toward automated vehicles (AVs). A comprehensive evaluation of recent AVs collision data indicates that modern automated driving systems are prone to rear-end collisions, usually leading to multiple-vehicle collisions. Moreover, most investigations into severe traffic conditions are confined to single-vehicle collisions. This work reviewed diverse techniques of existing literature to provide planning procedures for multiple vehicle cooperation and collision avoidance (MVCCA) strategies in AVs while also considering their performance and social impact viewpoints. Firstly, we investigate and tabulate the existing MVCCA techniques associated with single-vehicle collision avoidance perspectives. Then, current achievements are extensively evaluated, challenges and flows are identified, and remedies are intelligently formed to exploit a taxonomy. This paper also aims to give readers an AI-enabled conceptual framework and a decision-making model with a concrete structure of the training network settings to bridge the gaps between current investigations. These findings are intended to shed insight into the benefits of the greater efficiency of AVs set-up for academics and policymakers. Lastly, the open research issues discussed in this survey will pave the way for the actual implementation of driverless automated traffic systems.

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“…Some articles review specific behaviours, such as car-following behaviour (Saifuzzaman & Zheng, 2014), lane changing behaviour (Koesdwiady et al, 2016), and intersection behaviour (Shirazi & Morris, 2016), while others review specific driver traits such as impulsiveness (Bıçaksız & Özkan, 2016), sensation seeking (Zhang et al, 2019), and aggressive driving behaviour (Alkinani, Khan & Arshad, 2020). Few articles review the use of machine learning-based technology in ITS Nguyen et al (2018), Martinez et al (2017), Alsrehin, Klaib & Magableh (2019) and Pamuła (2016), the evolution of vehicles' sensing technology and their effect on safety (Massaro et al, 2016), and collision avoidance in assistance systems for intelligent vehicles Dahl et al (2018), Zhao et al (2017) and Mukhtar, Xia & Tang (2015). The effect of policies on driver behaviour and safety has been reviewed in Shinar & Gurion (2019).…”

Section: Survey Analysismentioning

confidence: 99%

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

Al-Hussein

Kiah

Por

et al. 2021

PeerJ Computer Science

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In the plan and development of Intelligent Transportation Systems (ITS), understanding drivers behaviour is considered highly valuable. Reckless driving, incompetent preventive measures, and the reliance on slow and incompetent assistance systems are attributed to the increasing rates of traffic accidents. This survey aims to review and scrutinize the literature related to sensor-based driver behaviour domain and to answer questions that are not covered so far by existing reviews. It covers the factors that are required in improving the understanding of various appropriate characteristics of this domain and outlines the common incentives, open confrontations, and imminent commendations from former researchers. Systematic scanning of the literature, from January 2014 to December 2020, mainly from four main databases, namely, IEEEXplore, ScienceDirect, Scopus and Web of Science to locate highly credible peer-reviewed articles. Amongst the 5,962 articles found, a total of 83 articles are selected based on the author’s predefined inclusion and exclusion criteria. Then, a taxonomy of existing literature is presented to recognize the various aspects of this relevant research area. Common issues, motivations, and recommendations of previous studies are identified and discussed. Moreover, substantial analysis is performed to identify gaps and weaknesses in current literature and guide future researchers into planning their experiments appropriately. Finally, future directions are provided for researchers interested in driver profiling and recognition. This survey is expected to aid in emphasizing existing research prospects and create further research directions in the near future.

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A review of essential technologies for collision avoidance assistance systems

Cited by 17 publications

References 83 publications

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Study on a Right-Turning Intelligent Vehicle Collision Warning and Avoidance Algorithm Based on Monte Carlo Simulation

Multiple vehicle cooperation and collision avoidance in automated vehicles: survey and an AI-enabled conceptual framework

A systematic review on sensor-based driver behaviour studies: coherent taxonomy, motivations, challenges, recommendations, substantial analysis and future directions

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