5 papers
CAPS: Context-Aware Priority Sampling for Enhanced Imitation Learning in Autonomous Driving
Hamidreza Mirkhani, Behzad Khamidehi, Ehsan Ahmadi +6
In this paper, we introduce Context-Aware Priority Sampling (CAPS), a novel method designed to enhance data efficiency in learning-based autonomous driving systems. CAPS addresses…
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…
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…
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…
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…