Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach

Sarkar, Abhijit; Hickman, Jeffrey S.; McDonald, Anthony D.; Huang, Wenyan; Vogelpohl, Tobias; Markkula, Gustav

doi:10.1016/j.aap.2021.106055

Cited by 11 publications

(7 citation statements)

References 29 publications

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“…In fact, decision trees are also used in driving. For example, the studies [37][38] try to better understand drivers' responses to accident situations and model their behavior in naturalistic driving. Decision trees are also used in autonomous driving, proposing how autonomous cars can predict the intention of other vehicles and interact more safely with them in [39], or how create predictive model of lateral car control [40].…”

Section: Approach and Methodologymentioning

confidence: 99%

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

et al. 2023

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Learning from human driver's strategies for undertaking complex traffic scenarios has the potential to improve decision-making methods for designing ADAS systems, as well as for design selfdriving rules for automated vehicles. This paper proposes a human-like decision-making algorithm built up from human drivers experiential naturalistic driving. The approach of this work consists of exploring two main techniques. Firstly, the use of "think aloud protocol" to build a dataset based on naturalistic driving, capturing driver's intentions. Afterwards, the technique of decision tree is used to generate an algorithm to categorize driving patterns as a function of circumstantial driving parameters. The study is focused on simple roundabouts in presence of other vehicles. The decision tree is translated into algorithmic rules, where the tree pathways are represented as 'if-then' rules. Finally, the accuracy of the driver behavior model has been assessed, yielding promising results.

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Section: Approach and Methodologymentioning

confidence: 99%

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

et al. 2023

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“…A vehicle only by longitudinal emergency braking or laterally changing is unable to avoid collision accident, then the vehicle encounters obstacles ahead, and the active collision avoidance system by the relative position and relative velocity judge whether there is a collision threat or risk control vehicle automatic obstacle avoidance of collision if the vehicle environment to the condition of lane changing is not in conformity. It can control the automatic braking of the vehicle, comprehensively consider the injury degree of the accident, avoid the risk and reduce the collision injury as much as possible [ 35 ]. Many companies and researchers in the field of collision avoidance system found that most studies only consider a safe distance, longitudinal or transverse collision, etc., without considering the collision caused by the crew casualties, but in the real scenario, under a lot of emergency cars, collision cannot be avoided when the driver is easy to emotional tension.…”

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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“…However, this is not a complete answer. For, there remain many individual forms of incompetence on our roadways, and Senders would be the first to acknowledge its presence and would applaud the more sophisticated on-road evaluations that he himself helped pioneer (see Sarkar et al, 2021). He dedicated his life to the dissolution of such ignorance.…”

Section: Carrying Senders’ Legacy Forwardmentioning

confidence: 99%

Attribution Errors by People and Intelligent Machines

2021

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Objective To explore the ramifications of attribution errors (AEs), initially in the context of vehicle collisions and then to extend this understanding into the broader and diverse realms of all forms of human–machine interaction. Background This work focuses upon a particular topic that John Senders was examining at the time of his death. He was using the lens of attribution, and its associated errors, to seek to further understand and explore dyadic forms of driver collision. Method We evaluated the utility of the set of Senders’ final observations on conjoint AE in two-vehicle collisions. We extended this evaluation to errors of attribution generally, as applicable to all human–human, human–technology, and prospectively technology–technology interactions. Results As with Senders and his many other contributions, we find evident value in this perspective on how humans react to each other and how they react to emerging forms of technology, such as autonomous systems. We illustrate this value through contemporary examples and prospective analyses. Applications The comprehension and mitigation of AEs can help improve all interactions between people, between intelligent machines and between humans and the machines they work with.

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Steering or braking avoidance response in SHRP2 rear-end crashes and near-crashes: A decision tree approach

Cited by 11 publications

References 29 publications

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

Think Aloud Protocol and Decision Tree for Driver Behavior Modeling at Roundabouts

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

Attribution Errors by People and Intelligent Machines

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