Connected and Autonomous Vehicle Cohort Speed Control Optimization via Neuroevolution

Abstract

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.

Neuroevolution Application to Collaborative and Heuristics-Based Connected and Autonomous Vehicle Cohort Simulation at Uncontrolled Intersection

“…While not yet as popular as conventional optimum techniques and deep learning, Neuroevolution can easily integrate within control architectures and heuristics to solve complex and system of systems control problems in a simpler manner while enabling adaptive and real-time performance with low compute power, as demonstrated in previous research [16]. Therefore, the Neuroevolution process proposed here is based on previous experience in developing predictive energy management functions for CAV cohorts and vehicles.…”

“…Indeed, mixing turning and non-turning operations inherently constrains the speed achievable for all vehicles and forces sub-optimal operation. These limitations prompted the authors to investigate the use of Artificial Intelligence, which demonstrated high energy savings with large heterogeneous cohorts operating at controlled intersection [16] and for local powertrain adaptive control [17]. Artificial intelligence offers a simple and fast development framework, low compute power requirements and high reusability thanks to its adaptiveness to a wide range of dynamics.…”

“…Artificial intelligence offers a simple and fast development framework, low compute power requirements and high reusability thanks to its adaptiveness to a wide range of dynamics. This is a critical factor in enabling our large heterogeneous CAV cohorts operation demonstrator [16] to be adapted to the uncontrolled intersection use case while retaining its inherent capabilities to deal with noise and dynamics perturbation as demonstrated when simulated within a high-fidelity chassis simulation in CarSim.…”

Idle Speed Control of Gasoline Engine Based on Fuzzy Neural Network

“…The fuel supply system is composed of pressure regulator components, gasoline pipe components, gasoline pump components, fuel injectors and other components. The electronic control unit feeds back the current working state to the electronic control unit in the form of electrical signal [15][16]. Air intake, fuel injection and ignition control are all control variables of automobile engine idle speed control.…”