Search-based optimal motion planning for automated driving

TL;DR

This paper introduces a real-time, search-based motion planning framework for automated driving that efficiently handles urban constraints and long horizons, validated through realistic traffic simulations.

Contribution

It presents a novel combination of geometrical representation and A*-based algorithm with model predictive features for fast, robust, and constraint-aware motion planning in urban environments.

Findings

Capable of real-time planning over several hundred meters

Handles complex urban constraints like traffic lights and other vehicles

Effective in both fast and slow driving scenarios, including full stops

Abstract

This paper presents a framework for fast and robust motion planning designed to facilitate automated driving. The framework allows for real-time computation even for horizons of several hundred meters and thus enabling automated driving in urban conditions. This is achieved through several features. Firstly, a convenient geometrical representation of both the search space and driving constraints enables the use of classical path planning approach. Thus, a wide variety of constraints can be tackled simultaneously (other vehicles, traffic lights, etc.). Secondly, an exact cost-to-go map, obtained by solving a relaxed problem, is then used by A*-based algorithm with model predictive flavour in order to compute the optimal motion trajectory. The algorithm takes into account both distance and time horizons. The approach is validated within a simulation study with realistic traffic scenarios. We demonstrate the capability of the algorithm to devise plans both in fast and slow driving conditions, even when full stop is required.