• DOI:10.1109/IROS45743.2020.9341020
Corpus ID: 214693314

End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning

@article{Chen2020EndtoendAD,
  title={End-to-end Autonomous Driving Perception with Sequential Latent Representation Learning},
  author={Jian-Yu Chen and Zhuo Xu and M. Tomizuka},
  journal={2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  year={2020},
  pages={1999-2006}
}

• Jian-Yu Chen, Zhuo Xu, M. Tomizuka
• Published 2020
• Computer Science, Mathematics
• 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Current autonomous driving systems are composed of a perception system and a decision system. Both of them are divided into multiple subsystems built up with lots of human heuristics. An end-to-end approach might clean up the system and avoid huge efforts of human engineering, as well as obtain better performance with increasing data and computation resources. Compared to the decision system, the perception system is more suitable to be designed in an end-to-end framework, since it does not… Expand

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