Connected and Autonomous Vehicle Cohort Speed Control Optimization via Neuroevolution

Predictive Energy Management (PrEM) research is at the forefront of modern transportation’s energy consumption reduction efforts. The development of PrEM optimization algorithms has been tailored to selfish vehicle operation and implemented in the form of vehicle dynamics and/or adaptive powertrain control functions. With the progress in vehicle automation, this paper focuses on extending PrEM into the realm of a System of Systems (SoS). The proposed approach uses the shared information among Connected and Automated Vehicles (CAV) and the infrastructure to synthesize a reduced energy speed trajectory at the cohort level within urban environments. Neuroevolution is employed to incorporate a generalized optimum controller, robust to the emergent behaviors typical of multi-agents SoS. The authors demonstrated the use of heuristics and systems engineering processes in abstracting and integrating the resulting neural network within the control architecture, which enables novel added-value features such as green wave pass/fail classification and e-Horizon velocity prediction. The resulting controller is faster than real-time and was validated with a multi-agent simulation environment and on a real-world closed-loop track at the American Center for Mobility (ACM). The GM Bolt and Volt CAV mixed cohort testing at ACM demonstrated energy reductions from 7% to 22% depending on scenarios.