A Self-Supervised Learning Approach to Rapid Path Planning for Car-Like Vehicles Maneuvering in Urban Environment

TL;DR

This paper presents a novel self-supervised neural network method for rapid, feasible path planning of car-like vehicles in urban environments, effectively leveraging past experience to handle complex, dynamic traffic scenarios.

Contribution

Introduces a self-supervised learning approach for fast, reliable path planning that overcomes limitations of supervised methods and reduces computational costs in urban driving.

Findings

The method generates feasible paths rapidly in complex environments.

It effectively exploits past planning experience for improved performance.

Computational experiments confirm the approach's efficiency and reliability.

Abstract

An efficient path planner for autonomous car-like vehicles should handle the strong kinematic constraints, particularly in confined spaces commonly encountered while maneuvering in city traffic, and should enable rapid planning, as the city traffic scenarios are highly dynamic. State-of-the-art planning algorithms handle such difficult cases at high computational cost, often yielding non-deterministic results. However, feasible local paths can be quickly generated leveraging the past planning experience gained in the same or similar environment. While learning through supervised training is problematic for real traffic scenarios, we introduce in this paper a novel neural network-based method for path planning, which employs a gradient-based self-supervised learning algorithm to predict feasible paths. This approach strongly exploits the experience gained in the past and rapidly yields feasible maneuver plans for car-like vehicles with limited steering-angle. The effectiveness of such an approach has been confirmed by computational experiments.