Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model

Abstract

Safely interacting with other traffic participants is one of the core requirements for autonomous driving, especially in intersections and occlusions. Most existing approaches are designed for particular scenarios and require significant human labor in parameter tuning to be applied to different situations. To solve this problem, we first propose a learning-based Interaction Point Model (IPM), which describes the interaction between agents with the protection time and interaction priority in a unified manner. We further integrate the proposed IPM into a novel planning framework, demonstrating its effectiveness and robustness through comprehensive simulations in highly dynamic environments.

Planning Speed Mode of All-Wheel Drive Autonomous Vehicles Considering Complete Constraint Set

“…In [7], the reference path is followed by low-level tracking using PID controllers. In [13], a learning-based Interaction Point Model (IPM) describes the interaction between agents. In [9], the Nonlinear Model Predictive Control (NMPC) strategy was aimed at controlling a small-scale car model for autonomous racing competitions.…”