Intersection eco-driving strategies under mixed traffic environment: An novel cooperation of traffic signal and vehicle trajectory planning

In a mixed traffic environment where connected and autonomous vehicles (CAVs) coexist with human-driven vehicles (HVs), the utilization of CAV information is important for improving eco-driving and reducing energy consumption at signalized intersections. This paper proposes a two-layer control method for signalized intersections that uses CAV information. The upper layer generates a dynamic signal NEMA timing scheme by estimating the virtual arrival times of both HVs and CAVs, to minimize the total vehicle delays. This is achieved through a hybrid heuristic algorithm that integrates sequential genetic algorithms (GA) and particle swarm algorithms (PSO) to identify the optimal signal scheme. The lower layer develops a generic distributed CAV eco-driving strategy based on the optimal signal timings. The strategy considers various factors such as signal state, queue information, the preceding vehicle, and collision avoidance, to optimize the ecological trajectory of CAVs under the mixed traffic flow. An event-triggered update reference eco-trajectory rule is applied to reduce computational cost and handle the impact of traffic uncertainty. Finally, comparative analyses with fixed controls, induction timing controls and max pressure controls show that the proposed method can reduce the average vehicle delays, fuel consumption, and emissions across different traffic conditions and varying CAV penetration rates.