KAIST CUVIC Lab

Connected and automated vehicles (CAVs) can plan and actuate control that explicitly considers performance, system safety, and actuation constraints in a manner more efficient than their human-driven counterparts. In particular, eco-driving is enabled through connected exchange of information from signalized corridors that share their upcoming signal phase and timing (SPaT). This is accomplished in the proposed control approach, which follows first principles to plan a free-flow acceleration-optimal trajectory through green traffic light intervals by Pontryagin’s Minimum Principle in a feedback manner. Urban conditions are then imposed from exogeneous traffic comprised of a mixture of human-driven vehicles (HVs) - as well as other CAVs. As such, safe disturbance compensation is achieved by implementing a model predictive controller (MPC) to anticipate and avoid collisions by issuing braking commands as necessary. The control strategy is experimentally vetted through vehicle-in-the-loop (VIL) of a prototype CAV that is embedded into a virtual traffic corridor realized through microsimulation. Up to 36% fuel savings are measured with the proposed control approach over a human-modelled driver, and it was found connectivity in the automation approach improved fuel economy by up to 26% over automation without. Additionally, the passive energy benefits realizable for human drivers when driving behind downstream CAVs are measured, showing up to 22% fuel savings in a HV when driving behind a small penetration of connectivity-enabled automated vehicles.

This paper formulates energy efficient driving of gasoline and electric powered vehicles as optimal control problems of various complexity. We show minimizing aerodynamic drag can maximize utilization of energy available at the wheel and requires low and constant speeds. By employing optimal control theory we show periods of maximal acceleration, maximal braking, and coasting often accompany constant speed cruising to satisfy boundary conditions on the states (bang-singular-bang optimal control). In the case of gasoline engine vehicles, analytical optimal control derivations show that pulse and glide operation of the engine while cruising can further reduce fuel use (chattering optimal control). For electric vehicles (EV), quadratic rather than linear dependence of energy use on control input results in different eco-driving patterns from gasoline engine vehicles. For EVs, analytical solution to the two point boundary value optimal control problem could be obtained after model simplification which is compared to numerical solution based on a more accurate model. We also evaluate optimal control solution in the presence of state constraints for EVs. Several simulation case studies are presented to showcase the energy efficiency gains with proposed eco driving strategies.

Verification and validation of connected and automated vehicle (CAV) control strategies are essential before real-world deployments. However, most prior work relies on simulation, and existing experiments often focus on simplified or isolated scenarios rather than corridor-scale traffic scenarios. In this study, we enhance a track-based X-in-the-loop (XIL) testing framework and systematically evaluate human-driven vehicles (HVs), connected human-driven vehicles (CHVs), and CAVs in a two-lane urban corridor under varying traffic conditions. Using Chicago Roosevelt Road as a reference, we generate realistic multi-lane traffic, including lane changes by HVs and eco-driving behaviors of CAVs. We prepare a new electric vehicle platform with full closed-loop control, furthermore we enhance a HoloLens 2 mixed-reality interface to render multi-lane virtual traffic in the driver’s field of view. The experimental results show that connected eco-driving can reduce energy consumption for both ego-CHV and ego-CAV cases, achieving up to 21% and 14% savings, respectively, under medium traffic conditions. However, travel times increase due to driver speed adherence limitations for ego-CHVs and interactions with surrounding HVs (e.g., cut-ins and queue growth) for ego-CAVs. When CAV penetration increases to 40% (future scenarios), the downstream traffic-smoothing effect reduces queue formation and cut-ins, enabling the ego-CAV to follow smoother trajectories and achieve higher energy savings (up to 21%) without additional travel time penalty.