Advances in AI for Autonomous Driving

Seminar an der Universität des Saarlandes, Fachrichtung Informatik, LSF 154628

The topics for seminar time slots (see schedule) are as follows. Currently assigned topics are marked in red.  Selected background paper references for these topics are given below; these papers and the indicated topic reference papers in the table are available in the web or on request from seminar organizers.

Topic #

Topic

1

Multi-Agent Behavior Prediction (1)

Xu, C. et al.  (2024): Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational Reasoning. Neural Networks, 170. Elsevier.  Ref

2

Multi-Agent Behavior Prediction (2)

Chen, J., Wang, Z., Wang, J., & Cai, B. (2024). Q‐EANet: Implicit social modeling for trajectory prediction via experience‐anchored queries. IET Intelligent Transport Systems, 18(6). Wiley. Ref

3

Multi-Agent Behavior Prediction and Navigation Planning: Ethical Aspects

Geisslinger, M., Poszler, F., & Lienkamp, M. (2023). An Ethical Trajectory Planning Algorithm for Autonomous Vehicles. Nature Machine Intelligence, 5(2), Nature.  Ref

4

Learning and Planning for Safe Navigation (1)

Hu, Y.  et al. (2023). Planning-oriented autonomous driving. Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Ref

5

Learning and Planning for Safe Navigation (2)

Liu, H., et al.  (2024). Hybrid-Prediction Integrated Planning for Autonomous Driving. arXiv preprint arXiv:2402.02426. Ref

6

Learning and Planning for Safe Navigation (3)

Danesh, M. H., Cai, P., & Hsu, D. (2023). LEADER: Learning Attention over Driving Behaviors for Planning under Uncertainty. Proc. of Machine Learning Research (PMLR) for Conference on Robot Learning, 205. Ref

7

Large Language Models for Safe Navigation (1)

Mao, J. et al. (2023). GPT-Driver: Learning to Drive with GPT. Proc. of NeurIPS Workshop on Foundation Models for Decision Making. Ref

8

Large Language Models for Safe Navigation (2)

Xu, Z., et al. (2024). DriveGPT4: Interpretable end-to-end autonomous driving via large language model. IEEE Robotics and Automation Letters. Ref

9

Large Language Models for Safe Navigation (3)

Shah, D. et al. (2023). Navigation with Large Language Models: Semantic Guesswork as a Heuristic for Planning. Proc. of Machine Learning Research (PMLR) for Conference on Robot Learning.  Ref

10

Large Language Models for Safe Navigation (4)

Mao, J., et al. (2024). A Language Agent for Autonomous Driving. Proc. Conference on Language Modeling (COLM). Ref

Selected Background Papers:

  1. Chen, L., et al. (2024). End-to-end autonomous driving: Challenges and frontiers. IEEE Transactions on Pattern Analysis and Machine Intelligence. IEEE. Ref
  2. Yurtsever, E. et al. (2020). A Survey of Autonomous Driving: Common Practices and Emerging Technologies. IEEE Access, 8. IEEE.  Ref
  3. Zhang, C., & Berger, C. (2023). Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review. IEEE Transactions on Intelligent Transportation Systems. IEEE. Ref
  4. Minaee, S., et al. (2024). Large Language Models: A Survey. arXiv preprint arXiv:2402.06196. Ref
  5. Yang, Z. et al. (2023). LLM4Drive: A Survey of Large Language Models for Autonomous Driving. arxiv preprint arxiv:2311.01043  Ref
  6. Garcez, A.D.A., & Lamb, L.C. (2023). Neurosymbolic AI: The 3rd Wave. Artificial Intelligence Review. Springer.  Ref

     

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