Prototype Network for Few-Shot Hazard Assessment of Vehicle Lane Changes in Risk Field

Abstract

It is well known that road traffic safety is one of the crucial topics in the field of automotive safety in assisted driving. In the face of complex traffic scenarios, there are still a large number of unsolved problems in the identification of vehicle risk levels. Given the difficulty of obtaining sufficient data for model training in many vehicle risk scenarios, it is important to correctly identify the vehicle risk posed through few-shot testing. The main idea of this method is to learn the class prototype of each risk state of a vehicle through encoding and mapping the vehicle’s characteristic parameters in the risk field and identifying the risk posed by few-shot vehicle lane changes through the application of the training of a prototype network. The latest results improve the efficiency of identifying risk in few-shot lane changes, which can provide practical guidance for risk identification and decision analysis in safe lane-change trigger scenarios based on the expected functional safety and provide theoretical support for the development of L3+ autonomous vehicles.