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arXiv · 2509.15400

Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities

Eric Aislan Antonelo·Gustavo Claudio Karl Couto·Christian Möller
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Abstract

Standard Behavior Cloning (BC) fails to learn multimodal driving decisions, where multiple valid actions exist for the same scenario. We explore Implicit Behavioral Cloning (IBC) with Energy-Based Models (EBMs) to better capture this multimodality. We propose Data-Augmented IBC (DA-IBC), which improves learning by perturbing expert actions to form the counterexamples of IBC training and using better initialization for derivative-free inference. Experiments in the CARLA simulator with Bird's-Eye View inputs demonstrate that DA-IBC outperforms standard IBC in urban driving tasks designed to evaluate multimodal behavior learning in a test environment. The learned energy landscapes are able to represent multimodal action distributions, which BC fails to achieve.

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Eric Aislan Antonelo, Gustavo Claudio Karl Couto, Christian Möller. 2025-09-18. Exploring multimodal implicit behavior learning for vehicle navigation in simulated cities. https://arxiv.org/abs/2509.15400

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