Chance-constrained eco-driving control of connected autonomous vehicles in mixed traffic environment at signalized intersections with uncertain signal timings

At signalized intersections, Connected Autonomous Vehicles (CAV) technology allows vehicle trajectory optimization using signal phasing and timing (SPaT) information, thereby reducing energy consumption caused by unnecessary deceleration and acceleration both upstream and downstream of signalized intersections. However, in widely deployed actuated traffic signal controllers, accurate and timely SPaT information is often unavailable for CAVs due to continuous adjustments in phase durations based on real-time traffic flow. Instead, only predicted SPaT information with inherent uncertainties can be provided within a specified confidence interval. Moreover, CAVs will coexist with human-driven vehicles (HVs) in mixed traffic environment, where the uncertainties of start-up lost time of HVs also present significant challenges to the trajectory optimization of CAVs. Hence, this paper proposes a chance-constrained eco-driving control for CAVs in mixed traffic environment with consideration of uncertainties in SPaT information and HVs. The method introduces a risk coefficient to represent the confidence level of CAVs regarding the probability distribution of uncertainties. The stochastic model predictive control (SMPC) with chance constraints is constructed to ensure that CAVs do not rear-end the preceding vehicle or exceed maximum acceleration/deceleration. The variable step size for SMPC is designed to enhance the performance of the proposed method while reducing the computation time and ensuring the real-time control. Safety analysis is conducted to demonstrate that the proposed method prevents CAVs from running red phases even under high risk coefficients. Numerical simulation under unsaturated, saturated and oversaturated traffic flow indicate that the proposed method effectively smooths the trajectories of CAVs and benefits the traffic efficiency. Comparing results at CAV penetration rates of 20 % and 80 % with 0 % (i.e., fully HVs), the proposed method reduces energy consumption by an average of 5.77 % and 10.24 % in the unsaturated traffic flow, 17.50 % and 31.32 % in the saturated traffic flow, and 22.99 % and 20.30 % in the oversaturated traffic flow, respectively.