Decision making and local trajectory planning for autonomous driving in off-road environment

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Different from urban environment with high-precision map, off-road environment lacks rich priori road structure information and precise location information, which is a huge challenge for autonomous driving. To solve these problems, this paper proposes a real-time decision-making and local trajectory planning method based on a predefined global reference line. According to the reference line, a cluster of parallel offset curves with corresponding obstacle and deviation costs are generated. Then, an appropriate parallel line with minimum cost is selected as the preliminary decision, and the target position with a matched speed on this line is choosed for local planning. Finally, the lateral and longitudinal trajectories planning is carried out with the initial constraint and target constraint of the quintic polynomials. The proposed method is verified in a 3.6 km bumpy off-road environment, including straight road, curves, continuous U-turn and other driving conditions. The maximum autonomous driving speed of an small-medium-sized platform can reach 30 km/h under the condition of stable and smooth driving.

Original languageEnglish
Title of host publicationProceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1180-1186
Number of pages7
ISBN (Electronic)9781728180250
DOIs
Publication statusPublished - 27 Nov 2020
Event3rd International Conference on Unmanned Systems, ICUS 2020 - Harbin, China
Duration: 27 Nov 202028 Nov 2020

Publication series

NameProceedings of 2020 3rd International Conference on Unmanned Systems, ICUS 2020

Conference

Conference3rd International Conference on Unmanned Systems, ICUS 2020
Country/TerritoryChina
CityHarbin
Period27/11/2028/11/20

Keywords

  • Autonomous driving
  • Local trajectory planning
  • Off-road
  • Parallel-line decision making

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