Domain Generalization for Vision-based Driving Trajectory Generation

• URL: http://arxiv.org/abs/2109.13858v1
• Date: Wed, 22 Sep 2021 07:49:07 GMT
• Title: Domain Generalization for Vision-based Driving Trajectory Generation
• Authors: Yunkai Wang, Dongkun Zhang, Yuxiang Cui, Zexi Chen, Wei Jing, Junbo Chen, Rong Xiong, Yue Wang
• Abstract summary: We propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments. We leverage an adversarial learning approach to train a trajectory generator as the decoder. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation.
• Score: 9.490923738117772
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the challenges in vision-based driving trajectory generation is dealing with out-of-distribution scenarios. In this paper, we propose a domain generalization method for vision-based driving trajectory generation for autonomous vehicles in urban environments, which can be seen as a solution to extend the Invariant Risk Minimization (IRM) method in complex problems. We leverage an adversarial learning approach to train a trajectory generator as the decoder. Based on the pre-trained decoder, we infer the latent variables corresponding to the trajectories, and pre-train the encoder by regressing the inferred latent variable. Finally, we fix the decoder but fine-tune the encoder with the final trajectory loss. We compare our proposed method with the state-of-the-art trajectory generation method and some recent domain generalization methods on both datasets and simulation, demonstrating that our method has better generalization ability.

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