An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

Hang, J; Yan, Xuedong; Li, Xiaomeng; Duan, Ke; Yang, Jingsi; Xue, Qingwu

doi:10.1016/j.jsr.2021.12.023

Cited by 17 publications

(11 citation statements)

References 59 publications

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“…Furthermore, when the TTC thresholds were set at 2 s and 3 s, there was no significant difference in the number of rear-end collisions, collision probability, or collision severity between the two threshold conditions. As a result, the study recommends setting the TTC threshold at 2 s as the risk judgment criteria for the AEB system to avoid larger threshold values causing false alarms [ 35 ].…”

Section: Discussionmentioning

confidence: 99%

“…On the other hand, a smaller TTC threshold provides accurate identification of collision risks but reduces system intervention ability. In the research on the warning systems in vehicles, commonly used TTC thresholds range from 1 to 3 s [ 35 , 36 , 37 ]. This study examined the effects of different TTC thresholds (i.e., 1 s, 2 s, and 3 s) on the effectiveness of the AEB.…”

Section: Methodsmentioning

confidence: 99%

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Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Zhao,

Li,

Huang

et al. 2023

Sensors

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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.

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Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Zhao,

Li,

Huang

et al. 2023

Sensors

Self Cite

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“…If the driver fails to take timely measures to avoid collision after exceeding the safe range, the active collision avoidance system of the vehicle will automatically take over the vehicle to avoid the accident risk. In some cases, vehicles cannot avoid rear-end collision by braking [ 33 , 34 ]. When vehicles are parallel or overtaking, lateral collision accidents are easy to occur.…”

Section: Related Workmentioning

confidence: 99%

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

Xiao

2022

Computational Intelligence and Neuroscience

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The age of algorithms is here, and it is really changing people’s lives. More and more ethical problems related to algorithms have attracted people’s attention, but the related ethical research is still far behind the research of algorithms. As more intelligent algorithms emerge in an endless stream, there will also be a lot of algorithmic ethical issues. On the other hand, with the continuous improvement of the development level of the automobile industry, people have a stronger demand for the safety and stability of modern transportation, and more and more autonomous driving technology has been promoted and applied in the market. At present, most of the studies on the longitudinal collision avoidance system of vehicles use collision warning or emergency braking to avoid collision. However, when the vehicle is in a special situation such as high speed and slippery road, emergency steering is more effective. In order to further improve the vehicle safety and ethical algorithm design points, this article revolves around vehicle lateral active collision avoidance control method research, the collision avoidance decision-making, and path planning and collision avoidance transverse vehicle longitudinal motion control is analyzed, and based on automated driving simulation experiment, the tests carried out to verify the designed control strategy. The experimental results show that the proposed method not only has a good effect of preventing automatic driving collision but also can meet the requirements of algorithm ethics. This research can effectively guide the research of algorithmic ethics in the field of autonomous driving and effectively reduce the occurrence of traffic accidents.

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“…Salvatore et al [6] compared rear-end collisions with and without AEB and found that both occupant OOP and whipping injuries were greater if AEB was applied. Hang et al [7] proposed a real-time automatic emergency system (RTAEB) to improve the sensitivity of AEB system control for rear-end collisions. Based on frontal collisions, Huang et al [8] collected time-to-collision and conflict distance data to develop a conflict distance model that could distinguish between frontal and side collisions.…”

Section: Introductionmentioning

confidence: 99%

Driver Injury from Vehicle Side Impacts When Automatic Emergency Braking and Active Seat Belts Are Used

Dao-wen

Liu

2023

Sensors

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As an advanced driver assistance system, automatic emergency braking (AEB) can effectively reduce accidents by using high-precision and high-coverage sensors. In particular, it has a significant advantage in reducing front-end collisions and rear-end accidents. Unfortunately, avoiding side collisions is a challenging problem for AEB. To tackle these challenges, we propose active seat belt pretensioning on driver injury in vehicles equipped with AEB in unavoidable side crashes. Firstly, records of impact cases from China’s National Automobile Accident In-Depth Investigation System were used to investigate a scenario in which a vehicle is impacted by an oncoming car after the vehicle’s AEB system is triggered. The scenario was created using PreScan software. Then, the simulated vehicles in the side impact were devised using a finite element model of the Toyota Yaris and a moving barrier. These were constructed in HyperMesh software along with models of the driver’s side seatbelt, side airbag, and side curtain airbag. Moreover, the models were verified, and driver out-of-position instances and injuries were evaluated in simulations with different AEB intensities up to 0.7 g for three typical side impact angles. Last but not least, the optimal combination of seatbelt pretensioning and the timing thereof for minimizing driver injury at each side impact angle was identified using orthogonal tests; immediate (at 0 ms) pretensioning at 80 N was applied. Our experiments show that our active seatbelt with the above parameters reduced the weighted injury criterion by 5.94%, 22.05%, and 20.37% at impact angles of 90°, 105°, and 120°, respectively, compared to that of a conventional seatbelt. The results of the experiment can be used as a reference to appropriately set the collision parameters of active seat belts for vehicles with AEB.

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An improved automated braking system for rear-end collisions: A study based on a driving simulator experiment

Cited by 17 publications

References 59 publications

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Numerical Analysis of an Autonomous Emergency Braking System for Rear-End Collisions of Electric Bicycles

Application of Machine Learning in Ethical Design of Autonomous Driving Crash Algorithms

Driver Injury from Vehicle Side Impacts When Automatic Emergency Braking and Active Seat Belts Are Used

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