A Data-Driven Approach for Autonomous Motion Planning and Control in Off-Road Driving Scenarios

TL;DR

This paper introduces a comprehensive data-driven framework for autonomous off-road vehicle motion planning and control, integrating environmental data, global and local planning, and real-time control to navigate complex terrains.

Contribution

It presents a novel integration of GIS data, dynamic programming, local path planning, and feedback control for off-road autonomous driving.

Findings

Successful simulation of terrain traversal in Oregon and Indiana.

Effective obstacle avoidance and trajectory tracking demonstrated.

Real-time updates improve planning robustness.

Abstract

This paper presents a novel data-driven approach to vehicle motion planning and control in off-road driving scenarios. For autonomous off-road driving, environmental conditions impact terrain traversability as a function of weather, surface composition, and slope. Geographical information system (GIS) and National Centers for Environmental Information datasets are processed to provide this information for interactive planning and control system elements. A top-level global route planner (GRP) defines optimal waypoints using dynamic programming (DP). A local path planner (LPP) computes a desired trajectory between waypoints such that infeasible control states and collisions with obstacles are avoided. The LPP also updates the GRP with real-time sensing and control data. A low-level feedback controller applies feedback linearization to asymptotically track the specified LPP trajectory. Autonomous driving simulation results are presented for traversal of terrains in Oregon and Indiana case studies.