1.2k citations · 2.1k across the 51 of their papers we have counts for
13 papers · 1 filter
A Shared Representation for Photorealistic Driving Simulators
Saeed Saadatnejad, Siyuan Li, Taylor Mordan +1
A powerful simulator highly decreases the need for real-world tests when training and evaluating autonomous vehicles. Data-driven simulators flourished with the recent advancement…
Do Pedestrians Pay Attention? Eye Contact Detection in the Wild
Younes Belkada, Lorenzo Bertoni, Romain Caristan +2
In urban or crowded environments, humans rely on eye contact for fast and efficient communication with nearby people. Autonomous agents also need to detect eye contact to interact…
Causal Imitative Model for Autonomous Driving
Mohammad Reza Samsami, Mohammadhossein Bahari, Saber Salehkaleybar +1
Imitation learning is a powerful approach for learning autonomous driving policy by leveraging data from expert driver demonstrations. However, driving policies trained via imitati…
Vehicle trajectory prediction works, but not everywhere
Mohammadhossein Bahari, Saeed Saadatnejad, Ahmad Rahimi +4
Vehicle trajectory prediction is nowadays a fundamental pillar of self-driving cars. Both the industry and research communities have acknowledged the need for such a pillar by prov…
DriverGym: Democratising Reinforcement Learning for Autonomous Driving
Parth Kothari, Christian Perone, Luca Bergamini +2
Despite promising progress in reinforcement learning (RL), developing algorithms for autonomous driving (AD) remains challenging: one of the critical issues being the absence of an…
Towards Robust and Adaptive Motion Forecasting: A Causal Representation Perspective
Yuejiang Liu, Riccardo Cadei, Jonas Schweizer +2
Learning behavioral patterns from observational data has been a de-facto approach to motion forecasting. Yet, the current paradigm suffers from two shortcomings: brittle under dist…
SVG-Net: An SVG-based Trajectory Prediction Model
Mohammadhossein Bahari, Vahid Zehtab, Sadegh Khorasani +3
Anticipating motions of vehicles in a scene is an essential problem for safe autonomous driving systems. To this end, the comprehension of the scene's infrastructure is often the m…
Keypoint Communities
Duncan Zauss, Sven Kreiss, Alexandre Alahi
We present a fast bottom-up method that jointly detects over 100 keypoints on humans or objects, also referred to as human/object pose estimation. We model all keypoints belonging…
Safety-aware Motion Prediction with Unseen Vehicles for Autonomous Driving
Xuanchi Ren, Tao Yang, Li Erran Li +2
Motion prediction of vehicles is critical but challenging due to the uncertainties in complex environments and the limited visibility caused by occlusions and limited sensor ranges…
Are socially-aware trajectory prediction models really socially-aware?
Saeed Saadatnejad, Mohammadhossein Bahari, Pedram Khorsandi +3
Our field has recently witnessed an arms race of neural network-based trajectory predictors. While these predictors are at the core of many applications such as autonomous navigati…
Interpretable Social Anchors for Human Trajectory Forecasting in Crowds
Parth Kothari, Brian Sifringer, Alexandre Alahi
Human trajectory forecasting in crowds, at its core, is a sequence prediction problem with specific challenges of capturing inter-sequence dependencies (social interactions) and co…
Injecting Knowledge in Data-driven Vehicle Trajectory Predictors
Mohammadhossein Bahari, Ismail Nejjar, Alexandre Alahi
Vehicle trajectory prediction tasks have been commonly tackled from two distinct perspectives: either with knowledge-driven methods or more recently with data-driven ones. On the o…
OpenPifPaf: Composite Fields for Semantic Keypoint Detection and Spatio-Temporal Association
Sven Kreiss, Lorenzo Bertoni, Alexandre Alahi
Many image-based perception tasks can be formulated as detecting, associating and tracking semantic keypoints, e.g., human body pose estimation and tracking. In this work, we prese…