A Review of Motion Planning for Highway Autonomous Driving

By Laurene Claussmann, Marc Revilloud, Dominique Gruyer, Sebastien Glaser. IEEE transactions on intelligent transportation systems (Print), 2020

608 citations recorded by the source corpus.

Self-driving vehicles will soon be a reality, as main automotive companies have announced that they will sell their driving automation modes in the 2020s. This technology raises relevant controversies, especially with recent deadly accidents. Nevertheless, autonomous vehicles are still popular and attractive thanks to the improvement they represent to people’s way of life (safer and quicker transit, more accessible, comfortable, convenient, efficient, and environment-friendly). This paper presents a review of motion planning techniques over the last decade with a focus on highway planning. In the context of this article, motion planning denotes path generation and decision making. Highway situations limit the problem to high speed and small curvature roads, with specific driver rules, under a constrained environment framework. Lane change, obstacle avoidance, car following, and merging are the situations addressed in this paper. After a brief introduction to the context of autonomous ground vehicles, the detailed conditions for motion planning are described. The main algorithms in motion planning, their features, and their applications to highway driving are reviewed, along with current and future challenges and open issues.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/ec549287e13d82e1985ab436dd9234d54a4b1f4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

  1. Siyu Teng
  2. Peng Deng
  3. Bai Li
  4. Yunfeng Ai
  5. Dongsheng Yang
  6. Lingxi Li
  7. Zhe Xuanyuan
  8. Xuemin Hu

Citations in the corpus