Autonomous vehicles (AVs) have long been regarded as a future product that can improve traffic efficiency, contribute to environmental protection, and reduce road accidents. While we see sufficient maturity and improvement recently in the self-driving stack, the large-scale deployment of fully self-driving cars is still at its early stage. In the past years, ML-first solutions have been showing promising results scaling with the amount of data, not only in simulation but also in real-world, complex environments. We want to open up the discussion on the challenges that need to be solved in order to enable the scaling of AVs in the real world and to encourage new ideas regarding their interpretability and safety.

Schedule

Time Event             Speaker Description

08:50                       🎉 Opening Remarks

09:00 Invited Speaker 1: Yuning Chai "Scaling the AV Across the US"

09:30 Invited Speaker 2: Yin Zhou   "Label efficient Machine Learning for Autonomous Driving"

10:00 Invited Speaker 3: Bert De Brabandere "A Deep Dive into Multi-sensor Data Labeling

10:30                       ☕️ Coffee and Posters          

11:00 Invited Speaker 4: Adrien Gaidon "Geometric Foundation Models"

11:30 Invited Speaker 5: Fisher Yu         "Scalable 4D Scene Understanding with Less Hassle"

12:00 Invited Speaker 6: Alexander Amini     "Data-driven Simulation for Trustworthy Autonomous Vehicles"

12:30                       🥪 Lunch

13:30 Invited Speaker 9: Anca Dragan  "Robust Human Prediction Through Robust Representations"

14:00 Invited Speaker 8: Rowan McAllister "Behavior Models for Interactive AV Forecasting and Planning"

14:30 Invited Speaker 7: Kashyap Chitta "Driving with Attention"

15:00                       ☕️ Coffee and Posters          

15:30 Invited Speaker 10: Marco Pavone    "On the Scalability of Modular Autonomy Stacks"

16:00 Invited Speaker 11: Jamie Shotton  "Frontiers in End-to-End Learning for Autonomous Driving"

16:30                       🗣 Panel Discussion

17:10                       👏 Closing Remarks

Accepted Papers

F2BEV: Bird’s Eye View Generation from Surround-View Fisheye Camera Images for Automated Driving
Ekta U. Samani, Feng Tao, Harshavardhan R. Dasari, Sihao Ding, and Ashis G. Banerjee

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation
Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang, Huizi Mao, Daniela L. Rus, Song Han

Assessing the Impact of Perception Failures on the Safety of Autonomous Vehicles
Pasquale Antonante, Sushant Veer, Karen Leung, Xinshuo Weng, Luca Carlone, and Marco Pavone

Selective Communication for Cooperative Perception in End-to-End Autonomous Driving
Hsu-kuang Chiu and Stephen F. Smith

Generating Driving Scenes with Diffusion
Ethan Pronovost, Kai Wang, Nick Roy

A V2V-based Collaborative Navigation Framework for Connected Autonomous Vehicles in Occluded Scenarios
Leandro Parada, Hanlin Tian, Jose Escribano, Panagiotis Angeloudis

Towards Deep Adaptive LiDAR-Radar Fusion for 3D Object Detection
Jingyu Song, Lingjun Zhao, and Katherine A. Skinner

Verifiable Goal Recognition for Autonomous Driving with Occlusions
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang, Stefano V. Albrecht

Dian Chen Lu Gan Anthony Hu Izzeddin Teeti Xintao Yan Chongfeng Wei Gamal ELGHAZALY Kiran Sajjanshetty Nishant Rai Macheng Shen Samuel Swift-Glasman Moulay Amine Jaidi Sudip Pramanik Chaitanya Shinde Sindhu R Padakandla Kiran Sajjanshetty