Interactive Left-Turning of Autonomous Vehicles at Uncontrolled Intersections

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Abstract
This paper presents a novel interactive motion planning approach for left-turning of autonomous vehicles at uncontrolled intersections. A typical left-turning scenario that consists of agent vehicles and an autonomous ego vehicle is established. The autonomous ego vehicle aims to cross the uncontrolled intersection quickly and safely when meeting with the agent vehicles. A modified obstacle reciprocal collision avoidance (MORCA) prediction model is proposed to predict the trajectory of the agent vehicles with the considerations of longitudinal and lateral interaction-aware behaviors. The parameters of the MORCA prediction model are optimized using particle swarm optimization (PSO) with the inD datasets. Based on MORCA, a partially observable Markov decision process (POMDP) framework is established. The action and state spaces of the POMDP are extended reasonably to satisfy the requirement of real-time application. The simulation results show that the prediction precision is improved by 15% using the MORCA compared with the obstacle reciprocal collision avoidance (ORCA) prediction model. Meanwhile, the results manifest that the proposed MORCA-based POMDP planning method improves 43.8% in commuting efficiency compared with a traditional rule-based method, nearly as good as the vehicle-to-vehicle (V2V) communication is equipped. Note to Practitioners —The motivation of this article is to establish a safe and efficient planning framework for the left-turning of autonomous vehicles at uncontrolled intersections. Rule-based approaches were widely used in the previous studies but they are not optimal. In this paper, the lateral movements of vehicles at the intersection are considered for the first time. A MORCA prediction model is established and optimized to formulate a POMDP planning framework. The CARLA simulation demonstrates the effectiveness of the proposed MORCA-based POMDP planner. In future work, we will migrate the algorithm to a real-world test platform for experimentation.
Funding Information
  • Natural Science Foundation of China (U1964206)

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