The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.
A Review of Motion Planning for Highway Autonomous Driving
Open accessAbstract
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.
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Cites
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Making Bertha Drive—An Autonomous Journey on a Historic Route
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CommonRoad: Composable benchmarks for motion planning on roads
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Ethical Decision Making during Automated Vehicle Crashes
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Cited by
Deep Learning for Safe Autonomous Driving: Current Challenges and Future Directions
Muhammad, K., Ullah, A., Lloret, J., Ser, J. D., & de Albuquerque, V. H. C. (2021) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2020.3032227 · Cited by 659
Perception Task Offloading With Collaborative Computation for Autonomous Driving
Xiao, Z., Shu, J., Jiang, H., Min, G., Chen, H., & Han, Z. (2023) · IEEE Journal on Selected Areas in Communications · doi:10.1109/jsac.2022.3227027 · Cited by 394
Elallid, B. B., Benamar, N., Hafid, A. S., Rachidi, T., & Mrani, N. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2022.03.013 · Cited by 179
Explainable AI-based Federated Deep Reinforcement Learning for Trusted Autonomous Driving
Rjoub, G., Bentahar, J., & Wahab, O. A. (2022) · 2022 International Wireless Communications and Mobile Computing (IWCMC) · doi:10.1109/iwcmc55113.2022.9824617 · Cited by 44
Detecting 32 Pedestrian Attributes for Autonomous Vehicles
Mordan, T., Cord, M., Perez, P., & Alahi, A. (2022) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2021.3107587 · Cited by 36
Metadata
- Authors
- Claussmann, L., Revilloud, M., Gruyer, D., & Glaser, S.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2020-05
- Volume
- 21
- Issue
- 5
- Pages
- 1826-1848
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W2946231253
- Cited by
- 670
- References
- 6
- FWCI
- 24.38
- Type
- journal article
- Language
- en
- Open access
- green
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