Safety risk assessment for autonomous vehicle road testing.

Objective: Road testing can accelerate the development and validation of autonomous vehicles (AVs). AV road testing can come with high safety risks, particularly in a complex road traffic environment, due to the immaturity of AV technology. A priori safety risk assessments of the road traffic environment before AV road testing are of great importance, allow the quantifying of risk levels in different road scenarios, and provide guidelines for AV road testing in low to high-risk environments.

Driver Intent-Based Intersection Autonomous Driving Collision Avoidance Reinforcement Learning Algorithm.

With the rapid development of artificial intelligent technology, the deep learning method is widely applied to predict human driving intentions due to its relative accuracy of prediction, which is one of critical links for security guarantee in the distributed, mixed driving scenario. In order to sense the intention of human-driven vehicles and reduce the self-driving collision avoidance rate, an improved intention prediction method for human-driving vehicles based on unsupervised, deep inverse reinforcement learning is proposed. Firstly, a contrast discriminator module was proposed to extract richer features.

A novel traffic conflict risk measure considering the effect of vehicle weight.

Introduction: Vehicle weight is deterministic to the impact force in collision, and thus the injury risk of vehicle occupants. In China, involvement of heavy vehicles in overall and fatal crashes are prevalent, even though heavy vehicles only constitute a small proportion of overall registered motor vehicles. However, vehicle weight is rarely considered in the existing traffic conflict risk prediction and assessment models because of the unavailability of required data.

Performance evaluation of surrogate measures of safety with naturalistic driving data.

Surrogate measures of safety (SMoS) play an important role in detecting traffic conflicts and in traffic safety assessment. However, the underlying assumptions of SMoS are different and a certain SMoS may be adequate/inadequate for different applications. A comprehensive approach to evaluate the validity and applicability of SMoS is lacking in the literature.