Advances in AI for Autonomous Driving

Seminar an der Universität des Saarlandes, Fachrichtung Informatik,  LSF 145839

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

Pedestrian Behavior Prediction: Trajectory (1)

Sighencea, B.I., Stanciu, I.R., & Căleanu, C.D. (2023). D-STGCN: Dynamic Pedestrian Trajectory Prediction Using Spatio-Temporal Graph Convolutional Networks. Electronics, 12(3). MDPI.

2

Pedestrian Behavior Prediction: Trajectory (2)

Muscholl, N. et al. (2020). SIMP3: Social Interaction-Based Multi-Pedestrian Path Prediction By Self-Driving Cars. Proc. 10th IEEE International Symposium on Computational Intelligence in Vehicles and Transportation Systems (CIVTS). IEEE.

3

Pedestrian Behavior Prediction: Trajectory and Action (1)

Liang, J., et al. (2019). Peeking Into the Future: Predicting Future Person Activities and Locations in Videos. Proc. IEEE/CVF International Conference on Computer Vision and Pattern Recognition. IEEE
.

4

Pedestrian Behavior Prediction: Trajectory and Action (2)

Rasouli, A., & Kotseruba, I. (2023). PedFormer: Pedestrian Behavior Prediction via Cross-Modal Attention Modulation and Gated Multitask Learning. Proc. IEEE International Conference on Robotics and Automation (ICRA). IEEE. 

5

Safe Navigation of Autonomous Vehicles: Hybrid Learning and Planning (1)

Pusse, F. & Klusch, M. (2019): Hybrid Online POMDP Planning and Deep Reinforcement Learning for Safer Self-Driving Cars. Proc. 30th IEEE International Intelligent Vehicles Symposium (IV).

6

Safe Navigation of Autonomous Vehicles: Hybrid Learning and Planning (2)

Gupta, D.; Klusch, M. (2023): Hybrid Deep Reinforcement Learning and Planning for Safe and Comfortable Automated Driving.  Proc. 34th IEEE International Intelligent Vehicles Symposium (IV), IEEE
.

7

Safe Navigation of Autonomous Vehicles: Learning (1)

Huang, Z., et al. (2023). Conditional Predictive Behavior Planning with Inverse Reinforcement Learning for Human-Like Autonomous Driving. IEEE Transactions on Intelligent Transportation Systems. IEEE.

8

Safe Navigation of Autonomous Vehicles: Learning (2)

Crosato, L., et al. (2022). Interaction-Aware Decision-Making for Automated Vehicles Using Social Value Orientation. IEEE Transactions on Intelligent Vehicles, 8(2). IEEE. [suppl.]

9

Safe Navigation of Autonomous Vehicles: Planning

Li, C., et al. (2022). Efficient Game-Theoretic Planning With Prediction Heuristic for Socially-Compliant Autonomous Driving. IEEE Robotics and Automation Letters, 7(4). IEEE.

10

Safe Navigation of Autonomous Vehicles: Learning (3)

Valiente, R., et al. (2022). Robustness and Adaptability of Reinforcement Learning-Based Cooperative Autonomous Driving in Mixed-Autonomy Traffic. IEEE Open Journal of Intelligent Transportation Systems, 3. IEEE.

 

Selected Background Papers:

  1. Yurtsever, E. et al. (2020). A Survey of Autonomous Driving: Common Practices and Emerging Technologies. IEEE Access, 8. IEEE. https://ieeexplore.ieee.org/iel7/6287639/8948470/09046805.pdf 
  2. Zhang, C., & Berger, C. (2023). Pedestrian Behavior Prediction Using Deep Learning Methods for Urban Scenarios: A Review. IEEE Transactions on Intelligent Transportation Systems. IEEE. https://ieeexplore.ieee.org/iel7/6979/4358928/10149114.pdf
  3. Chib, P. S., & Singh, P. (2023). Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey. arXiv:2307.04370. https://arxiv.org/pdf/2307.04370
  4. Zhu, Z., & Zhao, H. (2021). A Survey of Deep RL and IL for Autonomous Driving Policy Learning. IEEE Transactions on Intelligent Transportation Systems, 23(9). https://arxiv.org/pdf/2101.01993  
  5. Yadav, P., Mishra, A., & Kim, S. (2023). A Comprehensive Survey on Multi-Agent Reinforcement Learning for Connected and Automated Vehicles. Sensors, 23(10), 4710. https://www.mdpi.com/1424-8220/23/10/4710/pdf
  6. Garcez, A.D.A., & Lamb, L.C. (2023). Neurosymbolic AI: The 3rd Wave. Artificial Intelligence Review, 1-20. https://arxiv.org/pdf/2012.05876.pdf%7CNeurosymbolic