Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories

• URL: http://arxiv.org/abs/2404.13347v1
• Date: Sat, 20 Apr 2024 11:05:47 GMT
• Title: Augmenting Safety-Critical Driving Scenarios while Preserving Similarity to Expert Trajectories
• Authors: Hamidreza Mirkhani, Behzad Khamidehi, Kasra Rezaee,
• Abstract summary: Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. We propose a method designed to maintain similarity with expert trajectory data.
• Score: 3.072340427031969
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Trajectory augmentation serves as a means to mitigate distributional shift in imitation learning. However, imitating trajectories that inadequately represent the original expert data can result in undesirable behaviors, particularly in safety-critical scenarios. We propose a trajectory augmentation method designed to maintain similarity with expert trajectory data. To accomplish this, we first cluster trajectories to identify minority yet safety-critical groups. Then, we combine the trajectories within the same cluster through geometrical transformation to create new trajectories. These trajectories are then added to the training dataset, provided that they meet our specified safety-related criteria. Our experiments exhibit that training an imitation learning model using these augmented trajectories can significantly improve closed-loop performance.

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