3 citations · 9 across the 11 of their papers we have counts for
11 papers
SkyDrive: Learning to Drive in a New City from Aerial Traffic Monitoring
Weijiang Xiong, Lan Feng, Alexandre Alahi +1
Autonomous driving has made remarkable progress through imitation learning with massive human demonstration data. However, a trained planner often degrades severely when applied to…
Representation Distribution Matching for One-Step Visual Generation
Lan Feng, Wuyang Li, Eloi Zablocki +2
We elucidate the design space of Representation Distribution Matching (RDM), our name for the paradigm that trains a one-step image generator by matching generated and reference fe…
MAD: Motion Appearance Decoupling for efficient Driving World Models
Ahmad Rahimi, Valentin Gerard, Eloi Zablocki +2
Recent video diffusion models generate photorealistic, temporally coherent videos, yet they fall short as reliable world models for autonomous driving, where structured motion and…
RAP: 3D Rasterization Augmented End-to-End Planning
Lan Feng, Yang Gao, Eloi Zablocki +5
Imitation learning for end-to-end driving trains policies only on expert demonstrations. Once deployed in a closed loop, such policies lack recovery data: small mistakes cannot be…
Towards Generalizable Trajectory Prediction Using Dual-Level Representation Learning And Adaptive Prompting
Kaouther Messaoud, Matthieu Cord, Alexandre Alahi
Existing vehicle trajectory prediction models struggle with generalizability, prediction uncertainties, and handling complex interactions. It is often due to limitations like compl…
Practice-Informed, Practice-Ready: An AI security incident taxonomy
Lukas Bieringer, Sean McGregor, Nicole Nichols +7
With the increasing prevalence of AI systems, several real-world AI security incidents have been reported. However, despite forthcoming legal mandates, the reporting and collection…
UniTraj: A Unified Framework for Scalable Vehicle Trajectory Prediction
Lan Feng, Mohammadhossein Bahari, Kaouther Messaoud Ben Amor +3
Vehicle trajectory prediction has increasingly relied on data-driven solutions, but their ability to scale to different data domains and the impact of larger dataset sizes on their…
Testing autonomous vehicles and AI: perspectives and challenges from cybersecurity, transparency, robustness and fairness
David Fernández Llorca, Ronan Hamon, Henrik Junklewitz +9
This study explores the complexities of integrating Artificial Intelligence (AI) into Autonomous Vehicles (AVs), examining the challenges introduced by AI components and the impact…
Manipulating Trajectory Prediction with Backdoors
Kaouther Messaoud, Kathrin Grosse, Mickael Chen +3
Autonomous vehicles ought to predict the surrounding agents' trajectories to allow safe maneuvers in uncertain and complex traffic situations. As companies increasingly apply traje…
Towards more Practical Threat Models in Artificial Intelligence Security
Kathrin Grosse, Lukas Bieringer, Tarek Richard Besold +1
Recent works have identified a gap between research and practice in artificial intelligence security: threats studied in academia do not always reflect the practical use and securi…
Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?
Yihong Xu, Loïck Chambon, Éloi Zablocki +4
Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, trackin…