Autonomous vehicles’ intended cooperative motion planning for unprotected turning at intersections

Abstract

Turning behaviour is one of the most challenging driving manoeuvres that take place at intersections. Autonomous vehicles (AVs) are often overly conservative in these scenarios as they compromised to others’ behaviour to realize behavioural consistency. This paper proposes an intended cooperative motion planning (ICMP) that can actively predict the intentions of interacting participants. This helps achieve socially compliant cooperation and enables each vehicle to converge upon a set of consistent behaviours. The ICMP framework divides the integrated driving process into two related modules: prediction and planning. These modules are integrated using the mechanism of intended cooperation, which first introduces the criteria for a cooperative response and then maps them in the motion planning space. The prediction module applies a Gaussian mixture model to learn human drivers’ behaviour to determine conflict points, which helps to narrow down the solution spaces. In the planning module, paths are represented by quartic Bézier curves and speed is modelled as a polynomial function. Optimizations can then be used that maximize the efficiency and smoothness for path planning, and comfort and efficiency for speed planning. The test results show that ICMP‐based AVs had consistent interactions with other vehicles. Moreover, when compared with a potential field‐based method, the ICMP‐based method had better performance in terms of safety, comfort and efficiency, especially when interacting with multiple oncoming vehicles.

A matched case-control analysis of autonomous vs human-driven vehicle accidents

“…The modifications of scenarios can present difficulty for AVs in perceiving and responding to them, thereby raising the risk of an accident 58 . Moreover, multiple oncoming HDVs and the complexity of such driving scenarios are a considerable challenge for AVs such as unprotected left turns at intersections 59 . These situations are complicated by factors like limited priority and variation in trajectories 60 .…”

Dynamic Trajectory Planning and Optimization for Automated Driving on Ice and Snow Covered Road

“…Studies [2] have shown that ISCR is the most common and serious type of traffic accidents caused by a variety of environments. The promotion of 5G and Artificial Intelligence (AI) has accelerated the development of AVs toward functionality and intelligence, but the safety, comfort, and stability of Intelligent Driving System still face challenges [3]. Therefore, planning the optimal trajectory for autonomous vehicles (AVs) on ISCR is a technical problem that must be solved at present.…”