Eco-Driving Control of Connected and Automated Vehicles using Neural Network based Rollout
This work addresses energy efficiency for autonomous vehicles, but it is incremental as it builds on existing optimization methods with a neural network adaptation.
The paper tackled the problem of high computational and memory requirements in eco-driving control for connected and automated vehicles by proposing a hierarchical multi-horizon optimization framework using a neural network, achieving comparable performance to stochastic optimization with negligible on-board memory.
Connected and autonomous vehicles have the potential to minimize energy consumption by optimizing the vehicle velocity and powertrain dynamics with Vehicle-to-Everything info en route. Existing deterministic and stochastic methods created to solve the eco-driving problem generally suffer from high computational and memory requirements, which makes online implementation challenging. This work proposes a hierarchical multi-horizon optimization framework implemented via a neural network. The neural network learns a full-route value function to account for the variability in route information and is then used to approximate the terminal cost in a receding horizon optimization. Simulations over real-world routes demonstrate that the proposed approach achieves comparable performance to a stochastic optimization solution obtained via reinforcement learning, while requiring no sophisticated training paradigm and negligible on-board memory.