Time-Dependent Hybrid-State A* and Optimal Control for Autonomous Vehicles in Arbitrary and Dynamic Environment

TL;DR

This paper introduces a novel planning framework combining a time-dependent hybrid-state A* algorithm with trajectory optimization, enabling autonomous vehicles to navigate complex, dynamic urban environments efficiently and safely.

Contribution

It presents a new method for generating drivable areas and a hybrid-state A* algorithm for precise maneuver planning in dynamic settings, improving decision speed and accuracy.

Findings

Efficient computation of driving maneuvers in dynamic environments.

Robustness demonstrated across various simulated urban scenarios.

Enhanced decision-making for autonomous vehicle control.

Abstract

The development of driving functions for autonomous vehicles in urban environments is still a challenging task. In comparison with driving on motorways, a wide variety of moving road users, such as pedestrians or cyclists, but also the strongly varying and sometimes very narrow road layout pose special challenges. The ability to make fast decisions about exact maneuvers and to execute them by applying sophisticated control commands is one of the key requirements for autonomous vehicles in such situations. In this context we present an algorithmic concept of three correlated methods. Its basis is a novel technique for the automated generation of a free-space polygon, providing a generic representation of the currently drivable area. We then develop a time-dependent hybrid-state A* algorithm as a model-based planner for the efficient and precise computation of possible driving maneuvers in arbitrary dynamic environments. While on the one hand its results can be used as a basis for making short-term decisions, we also show their applicability as an initial guess for a subsequent trajectory optimization in order to compute applicable control signals. Finally, we provide numerical results for a variety of simulated situations demonstrating the efficiency and robustness of the proposed methods.