103 citations · 112 across the 19 of their papers we have counts for
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Learning Soft Driving Constraints from Vectorized Scene Embeddings while Imitating Expert Trajectories
Niloufar Saeidi Mobarakeh, Behzad Khamidehi, Chunlin Li +6
The primary goal of motion planning is to generate safe and efficient trajectories for vehicles. Traditionally, motion planning models are trained using imitation learning to mimic…
Curb Your Attention: Causal Attention Gating for Robust Trajectory Prediction in Autonomous Driving
Ehsan Ahmadi, Ray Mercurius, Soheil Alizadeh +2
Trajectory prediction models in autonomous driving are vulnerable to perturbations from non-causal agents whose actions should not affect the ego-agent's behavior. Such perturbatio…
Confidence Aware Inverse Constrained Reinforcement Learning
Sriram Ganapathi Subramanian, Guiliang Liu, Mohammed Elmahgiubi +2
In coming up with solutions to real-world problems, humans implicitly adhere to constraints that are too numerous and complex to be specified completely. However, reinforcement lea…
Vectorized Representation Dreamer (VRD): Dreaming-Assisted Multi-Agent Motion-Forecasting
Hunter Schofield, Hamidreza Mirkhani, Mohammed Elmahgiubi +2
For an autonomous vehicle to plan a path in its environment, it must be able to accurately forecast the trajectory of all dynamic objects in its proximity. While many traditional m…
Validity Learning on Failures: Mitigating the Distribution Shift in Autonomous Vehicle Planning
Fazel Arasteh, Mohammed Elmahgiubi, Behzad Khamidehi +4
The planning problem constitutes a fundamental aspect of the autonomous driving framework. Recent strides in representation learning have empowered vehicles to comprehend their sur…
Analysis of a Modular Autonomous Driving Architecture: The Top Submission to CARLA Leaderboard 2.0 Challenge
Weize Zhang, Mohammed Elmahgiubi, Kasra Rezaee +8
In this paper we present the architecture of the Kyber-E2E submission to the map track of CARLA Leaderboard 2.0 Autonomous Driving (AD) challenge 2023, which achieved first place.…
Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee
Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert da…