Eco-Driving of Connected and Autonomous Vehicles with Sequence-to-Sequence Prediction of Target Vehicle Velocity

TL;DR

This paper presents a real-time eco-driving strategy for connected autonomous vehicles that predicts target vehicle velocity using sequence-to-sequence models, optimizing fuel efficiency in mixed traffic scenarios.

Contribution

It introduces a novel integration of sequence-to-sequence velocity prediction with receding-horizon control for eco-driving in mixed traffic environments.

Findings

The velocity prediction model achieves high accuracy in various traffic scenarios.

The eco-driving control reduces fuel consumption compared to baseline strategies.

The approach adapts effectively to both connected and unconnected surrounding vehicles.

Abstract

The Eco-Driving control problem seeks to perform fuel efficient speed planning for a Connected and Autonomous Vehicle (CAV) that can exploit information available from advanced mapping, and from Vehicle-to-Everything (V2X) communication. The ability of an Eco-Driving strategy to adapt in real time to variable traffic scenarios where surrounding vehicles can be either connected or unconnected is critical for further development and deployment of this technology in the transportation sector. In this work, the Eco-Driving strategy, formulated as a receding-horizon optimal control problem, is integrated with a target vehicle speed prediction model and solved via Dynamic Programming (DP) to determine the optimal speed trajectory in the presence of a human-driven target vehicle. An encoder-decoder architecture analyzes the patterns in the target vehicle velocity recorded over a historic window using a Gated-Recurrent-Unit (GRU) based encoder and generates an estimate of the future velocity trajectory using the GRU based decoder. A sensitivity study is done to analyze the effect of the historical and prediction windows on the accuracy of the velocity predictor. The proposed Eco-Driving controller is evaluated through microscopic simulations using a traffic simulator.