  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.
