Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic

• URL: http://arxiv.org/abs/2212.02224v1
• Date: Mon, 5 Dec 2022 12:56:42 GMT
• Title: Bi-Level Optimization Augmented with Conditional Variational Autoencoder for Autonomous Driving in Dense Traffic
• Authors: Arun Kumar Singh, Jatan Shrestha, Nicola Albarella
• Abstract summary: This paper presents a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting trajectory. Our approach runs in real-time using a custom GPU-accelerated batch, and a Variational Autoencoder learnt warm-start strategy. Our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.
• Score: 0.9281671380673306
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Autonomous driving has a natural bi-level structure. The goal of the upper behavioural layer is to provide appropriate lane change, speeding up, and braking decisions to optimize a given driving task. However, this layer can only indirectly influence the driving efficiency through the lower-level trajectory planner, which takes in the behavioural inputs to produce motion commands. Existing sampling-based approaches do not fully exploit the strong coupling between the behavioural and planning layer. On the other hand, end-to-end Reinforcement Learning (RL) can learn a behavioural layer while incorporating feedback from the lower-level planner. However, purely data-driven approaches often fail in safety metrics in unseen environments. This paper presents a novel alternative; a parameterized bi-level optimization that jointly computes the optimal behavioural decisions and the resulting downstream trajectory. Our approach runs in real-time using a custom GPU-accelerated batch optimizer, and a Conditional Variational Autoencoder learnt warm-start strategy. Extensive simulations show that our approach outperforms state-of-the-art model predictive control and RL approaches in terms of collision rate while being competitive in driving efficiency.

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