Driving Risk Field and Control Strategies for Autonomous Vehicles at a Signalized Intersection

Abstract

Driving pattern has been increasingly researched to improve driving safety and develop autonomous vehicles. Oriented towards the complex infrastructures at signalized intersections, this research digs into the risk sources brought by different kinds of road elements, including road lane markings, road curbs, median separators, signal timing, and neighboring vehicles around the ego car. Referring to vehicle speed both in the longitudinal and latitudinal dimensions, risk scope and distribution are quantified with the vehicle position of a torus with a Gaussian cross-section. Then, the risk is summed over all the road elements across all the points involved by the ego car, the level of which should be controlled within the threshold value when the ego vehicle explores to minimize trip delay. Thus, autonomous driving strategies are developed with respect to vehicle speed and steering angle. The proposed model is validated with NGSIM data, where a signalized intersection on Peachtree Street is selected and vehicles moving in different directions are analyzed. It is found that the proposed model manages to control vehicles with risk at the accepted level and to enhance the speed level as well as reduce acceleration fluctuations. This research contributes to improving autonomous driving against complex driving conditions for driving safety and efficiency.