Motion Planning for Autonomous Driving: The State of the Art and Future Perspectives

By Siyu Teng, Xuemin Hu, Peng Deng, Bai Li, Yunfeng Ai, Dongsheng Yang, Lingxi Li, Zhe Xuanyuan, F. Zhu, Long Chen. IEEE Transactions on Intelligent Vehicles, 2023

570 citations recorded by the source corpus.

Intelligent vehicles (IVs) have gained worldwide attention due to their increased convenience, safety advantages, and potential commercial value. Despite predictions of commercial deployment by 2025, implementation remains limited to small-scale validation, with precise tracking controllers and motion planners being essential prerequisites for IVs. This article reviews state-of-the-art motion planning methods for IVs, including pipeline planning and end-to-end planning methods. The study examines the selection, expansion, and optimization operations in a pipeline method, while it investigates training approaches and validation scenarios for driving tasks in end-to-end methods. Experimental platforms are reviewed to assist readers in choosing suitable training and validation strategies. A side-by-side comparison of the methods is provided to highlight their strengths and limitations, aiding system-level design choices. Current challenges and future perspectives are also discussed in this survey.

Subfields: Automotive Engineering, Computer Vision and Pattern Recognition, Social Psychology

View Paper: https://www.semanticscholar.org/paper/1c6fbf5c76aee77b539dc3f50991d7ac6c8356e8

How researchers see this paper

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

In Automotive Engineering

1. Kashyap Chitta
2. Andreas Geiger
3. Hongyang Li
4. Li Chen
5. Peng Wu
6. Bernhard Jaeger
7. Katrin Renz
8. Ping Luo

In Computer Vision and Pattern Recognition

1. Andreas Geiger
2. Hongyang Li
3. Ping Luo
4. Xiaogang Wang
5. Luc Van Gool
6. Kashyap Chitta
7. Dacheng Tao
8. Ming-Hsuan Yang

In Social Psychology

1. Kashyap Chitta
2. Andreas Geiger

Citations in the corpus

• End-to-End Autonomous Driving: Challenges and Frontiers (2023, background): “We would like to clarify the difference between our survey and previous related surveys [40], [41], [42], [43], [44], [45], [46], [47], [48].”
• Milestones in Autonomous Driving and Intelligent Vehicles: Survey of Surveys (2023, unknown): “pretability enhancement for a learning-based planner [143].”
• DriveLM: Driving with Graph Visual Question Answering (2023, background): “The inadequacy of generalization to the “long tail” of corner cases poses significant safety concerns to AD systems [8, 65, 66].”