Target Vehicle Motion Prediction-Based Motion Planning Framework for Autonomous Driving in Uncontrolled Intersections

This paper presents a motion-planning framework for urban autonomous driving at uncontrolled intersections. The intention and future state of the target vehicles are predicted using information obtained from the environment sensors. The target state prediction module employs an Interacting Multiple Model (IMM) filter to infer the intention of targets. The prediction results of each model of the IMM filter are fused to predict the future state of targets. The proposed predictor uses the intelligent driver model based-driver behavior model to construct the local filter of IMM. The driving mode decision is realized as a state machine consisting of two phases, `Approach' and `Risk Management'. The risk management phase is composed of two sub-modes, `Cross' and `Yield'. The state transition conditions between phases and modes are defined by introducing the concepts of `Critical gap' and `Follow-up gap'. Based on the determined driving mode, the motion planning module consists of two sub-modules for each phase. The required deceleration determination for the approach phase is proposed to consider the occluded region in order to prevent inevitable collisions caused by fast approaches. The model predictive controller for the risk management phase is designed to determine the desired acceleration to guarantee safety and prevent unnecessary deceleration simultaneously. Both computer simulation studies and vehicle tests are conducted to evaluate the proposed framework. The results indicate that the proposed framework ensures the safety at uncontrolled intersections with a driving pattern similar to that of a driver.

I. Introduction

Over the last decade, substantial research has been conducted in different fields of autonomous driving, such as perception, decision, and control [1]. The research field of autonomous driving has been extended from motorways to urban roads in order to ensure the safety in urban environments. Uncontrolled intersections are particularly risky areas in urban environments. Because there is no traffic signals or road signs to control the traffic at uncontrolled intersections. Several studies have been conducted to increase the safety in uncontrolled intersections. Two issues have been addressed in previous studies for the motion planning of autonomous vehicles at uncontrolled intersections [2], [3]: The first issue is inferring the intentions of other vehicles. The second issue is planning the desired motion, especially longitudinal motion.