ROSYOct 14, 2020

A Geometric Approach to On-road Motion Planning for Long and Multi-Body Heavy-Duty Vehicles

arXiv:2010.07133v2
Originality Incremental advance
AI Analysis

This addresses the specific challenge of autonomous motion planning for heavy-duty vehicles, which is an incremental improvement over existing methods focused on passenger cars.

The paper tackles the problem of on-road motion planning for long and multi-body heavy-duty vehicles like buses and tractor-trailers by proposing a geometric framework to design an optimization objective that finds the optimal trade-off between centering different axles in the lane, resulting in planned paths that considerably improve vehicle behavior by keeping the whole vehicle body centered.

Driving heavy-duty vehicles, such as buses and tractor-trailer vehicles, is a difficult task in comparison to passenger cars. Most research on motion planning for autonomous vehicles has focused on passenger vehicles, and many unique challenges associated with heavy-duty vehicles remain open. However, recent works have started to tackle the particular difficulties related to on-road motion planning for buses and tractor-trailer vehicles using numerical optimization approaches. In this work, we propose a framework to design an optimization objective to be used in motion planners. Based on geometric derivations, the method finds the optimal trade-off between the conflicting objectives of centering different axles of the vehicle in the lane. For the buses, we consider the front and rear axles trade-off, whereas for articulated vehicles, we consider the tractor and trailer rear axles trade-off. Our results show that the proposed design strategy results in planned paths that considerably improve the behavior of heavy-duty vehicles by keeping the whole vehicle body in the center of the lane.

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