The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Interpretable End-to-End Urban Autonomous Driving With Latent Deep Reinforcement Learning
Open accessAbstract
Unlike popular modularized framework, end-to-end autonomous driving seeks to solve the perception, decision and control problems in an integrated way, which can be more adapting to new scenarios and easier to generalize at scale. However, existing end-to-end approaches are often lack of interpretability, and can only deal with simple driving tasks like lane keeping. In this article, we propose an interpretable deep reinforcement learning method for end-to-end autonomous driving, which is able to handle complex urban scenarios. A sequential latent environment model is introduced and learned jointly with the reinforcement learning process. With this latent model, a semantic birdeye mask can be generated, which is enforced to connect with certain intermediate properties in today’s modularized framework for the purpose of explaining the behaviors of learned policy. The latent space also significantly reduces the sample complexity of reinforcement learning. Comparison tests in a realistic driving simulator show that the performance of our method in urban scenarios with crowded surrounding vehicles dominates many baselines including DQN, DDPG, TD3 and SAC. Moreover, through masked outputs, the learned model is able to provide a better explanation of how the car reasons about the driving environment.
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Cites
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Cited by
Atakishiyev, S., Salameh, M., Yao, H., & Goebel, R. (2021) · arXiv · doi:10.48550/arxiv.2112.11561 · Cited by 336
Explainability of Deep Vision-Based Autonomous Driving Systems: Review and Challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2022) · International Journal of Computer Vision · doi:10.1007/s11263-022-01657-x · Cited by 227
Explainability of deep vision-based autonomous driving systems: Review and challenges
Zablocki, É., Ben-Younes, H., Pérez, P., & Cord, M. (2021) · arXiv · doi:10.48550/arxiv.2101.05307 · Cited by 227
Metadata
- Authors
- Chen, J., Li, S. E., & Tomizuka, M.
- Venue
- IEEE Transactions on Intelligent Transportation Systems
- Published
- 2022-06
- Volume
- 23
- Issue
- 6
- Pages
- 5068-5078
- ISSN
- 1524-9050 (ISSN-L)
- 1558-0016
- OpenAlex
- W3128395826
- Cited by
- 322
- References
- 36
- FWCI
- 15.95
- Type
- journal article
- Language
- en
- Open access
- green
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