Papers
Topics
Authors
Recent
Gemini 2.5 Flash
Gemini 2.5 Flash
125 tokens/sec
GPT-4o
53 tokens/sec
Gemini 2.5 Pro Pro
42 tokens/sec
o3 Pro
4 tokens/sec
GPT-4.1 Pro
47 tokens/sec
DeepSeek R1 via Azure Pro
28 tokens/sec
2000 character limit reached

Human-Like Autonomous Driving on Dense Traffic (2310.02477v2)

Published 3 Oct 2023 in cs.RO

Abstract: This paper proposes a imitation learning model for autonomous driving on highway traffic by mimicking human drivers' driving behaviours. The study utilizes the HighD traffic dataset, which is complex, high-dimensional, and diverse in vehicle variations. Imitation learning is an alternative solution to autonomous highway driving that reduces the sample complexity of learning a challenging task compared to reinforcement learning. However, imitation learning has limitations such as vulnerability to compounding errors in unseen states, poor generalization, and inability to predict outlier driver profiles. To address these issues, the paper proposes mixture density network behaviour cloning model to manage complex and non-linear relationships between inputs and outputs and make more informed decisions about the vehicle's actions. Additional improvement is using collision penalty based on the GAIL model. The paper includes a simulated driving test to demonstrate the effectiveness of the proposed method based on real traffic scenarios and provides conclusions on its potential impact on autonomous driving.

Definition Search Book Streamline Icon: https://streamlinehq.com
References (35)
  1. Imitation learning: A survey of learning methods. ACM Computing Surveys (CSUR), 50(2):1–35, 2017.
  2. Dean A Pomerleau. Alvinn: An autonomous land vehicle in a neural network. Advances in neural information processing systems, 1, 1988.
  3. Apprenticeship learning via inverse reinforcement learning. In Proceedings of the twenty-first international conference on Machine learning, page 1, 2004.
  4. Probability-based decision making for automated highway driving. IEEE Transactions on Vehicular Technology, 43(3):626–634, 1994.
  5. Peter G Gipps. A model for the structure of lane-changing decisions. Transportation Research Part B: Methodological, 20(5):403–414, 1986.
  6. General lane-changing model mobil for car-following models. Transportation Research Record, 1999(1):86–94, 2007.
  7. Prediction based decision making for autonomous highway driving. In 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC), pages 138–145. IEEE, 2022.
  8. J. Ross Quinlan. Induction of decision trees. Machine learning, 1(1):81–106, 1986.
  9. Tin Kam Ho. Random decision forests. In Proceedings of 3rd international conference on document analysis and recognition, volume 1, pages 278–282. IEEE, 1995.
  10. A novel lane change decision-making model of autonomous vehicle based on support vector machine. IEEE Access, 7:26543–26550, 2019.
  11. Fanlin Meng et al. Dynamic decision making in lane change: Game theory with receding horizon. In 2016 UKACC 11th International Conference on Control (CONTROL), pages 1–6. IEEE, 2016.
  12. A binary decision model for discretionary lane changing move based on fuzzy inference system. Transportation Research Part C: Emerging Technologies, 67:47–61, 2016.
  13. Monte-carlo planning in large pomdps. Advances in neural information processing systems, 23, 2010.
  14. Human-like decision-making for automated driving in highways. In 2019 IEEE Intelligent Transportation Systems Conference (ITSC), pages 2087–2094. IEEE, 2019.
  15. Christopher M Bishop. Mixture density networks. 1994.
  16. Sampo Juhani Kuutti. End-to-end deep learning control for autonomous vehicles. PhD thesis, University of Surrey, 2022.
  17. Planning for autonomous driving via interaction-aware probabilistic action policies. IEEE Access, 10:81699–81712, 2022.
  18. Adversarial mixture density networks: Learning to drive safely from collision data. In 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), pages 705–711. IEEE, 2021.
  19. D-gan: autonomous driving using generative adversarial networks, 2018.
  20. Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114, 2013.
  21. Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862, 2017.
  22. Improved techniques for training gans. Advances in neural information processing systems, 29, 2016.
  23. Wasserstein generative adversarial networks. In International conference on machine learning, pages 214–223. PMLR, 2017.
  24. Mixture density generative adversarial networks. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 5820–5829, 2019.
  25. New approach in human-ai interaction by reinforcement-imitation learning. Applied Sciences, 11(7):3068, 2021.
  26. Multi-agent imitation learning for driving simulation. In 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 1534–1539. IEEE, 2018.
  27. A reduction of imitation learning and structured prediction to no-regret online learning. In Proceedings of the fourteenth international conference on artificial intelligence and statistics, pages 627–635. JMLR Workshop and Conference Proceedings, 2011.
  28. Random expert distillation: Imitation learning via expert policy support estimation. In International Conference on Machine Learning, pages 6536–6544. PMLR, 2019.
  29. Mixgail: Autonomous driving using demonstrations with mixed qualities. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pages 5425–5430. IEEE, 2020.
  30. Oversaturated freeway flow algorithm for use in next generation simulation. Transportation Research Record, 2088(1):68–79, 2008.
  31. The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2118–2125, 2018.
  32. Carla: An open urban driving simulator. In Conference on robot learning, pages 1–16. PMLR, 2017.
  33. Airsim: High-fidelity visual and physical simulation for autonomous vehicles. In Field and service robotics, pages 621–635. Springer, 2018.
  34. Pablo Alvarez Lopez et al. Microscopic traffic simulation using sumo. In 2018 21st international conference on intelligent transportation systems (ITSC), pages 2575–2582. IEEE, 2018.
  35. The highd dataset: A drone dataset of naturalistic vehicle trajectories on german highways for validation of highly automated driving systems. In 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pages 2118–2125. IEEE, 2018.

Summary

We haven't generated a summary for this paper yet.

Ai Sparkles Streamline Icon: https://streamlinehq.com Summarize Now
X Twitter Logo Streamline Icon: https://streamlinehq.com

Tweets