Learning Sampling Distribution and Safety Filter for Autonomous Driving with VQ-VAE and Differentiable Optimization

Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribu...

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Bibliographic Details
Published in:Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems pp. 3260 - 3267
Main Authors: Idoko, Simon, Sharma, Basant, Singh, Arun Kumar
Format: Conference Proceeding
Language:English
Published: IEEE 14.10.2024
Subjects:
Autoencoders
Autonomous vehicles
Collision avoidance
Cost function
Driver behavior
Intelligent robots
Safety
Self-supervised learning
Trajectory
Vectors
ISSN:2153-0866
Online Access:Get full text
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Summary:Sampling trajectories from a distribution followed by ranking them based on a specified cost function is a common approach in autonomous driving. Typically, the sampling distribution is hand-crafted (e.g a Gaussian, or a grid). Recently, there have been efforts towards learning the sampling distribution through generative models such as Conditional Variational Autoencoder (CVAE). However, these approaches fail to capture the multi-modality of the driving behaviour due to the Gaussian latent prior of the CVAE. Thus, in this paper, we re-imagine the distribution learning through vector quantized variational autoencoder (VQ-VAE), whose discrete latent-space is well equipped to capture multi-modal sampling distribution. The VQ-VAE is trained with demonstration data of optimal trajectories. We further propose a differentiable optimization based safety filter to minimally correct the VQ-VAE sampled trajectories to ensure collision avoidance. We use backpropagation through the optimization layers in a self-supervised learning set-up to learn good initialization and optimal parameters of the safety filter. We perform extensive comparisons with state-of-the-art CVAE-based baseline in dense and aggressive traffic scenarios and show a reduction of up to 12 times in collision-rate while being competitive in driving speeds.
ISSN:2153-0866
DOI:10.1109/IROS58592.2024.10801610