An Eco-Driving Approach With Flow Uncertainty Tolerance for Connected Vehicles Against Waiting Queue Dynamics on Arterial Roads

Deep reinforcement learning-based eco-driving control for connected electric vehicles at signalized intersections considering traffic uncertainties

“…It can be found that the OCP contains nonlinear constraints from traffic lights and preceding vehicles, and consequently, it remains challenging to directly solve it using traditional optimization algorithms. Previous studies attempted to solve this problem by proposing modified optimization frameworks or algorithms, such as iterative DP algorithm and hierarchical optimization framework [23,26]. However, any improvement of the calculation efficiency is reported to be achieved when the optimality of performance is sacrificed.…”

“…To fully explore the adaptability of the proposed method, the energy consumption, travel time, idling time proportion and low-speed time proportion are summarized for comparison. The speed boundary (1.39 m/s) is selected to judge the vehicle in a low-speed state [26].…”

“…In [26], a long short-term memory neural network is implemented to predict dynamics traffic flow, thereby promoting to waiting queue prediction. However, the above-mentioned methods are typically executed by assuming that a waiting queue is already generated or will emerge before signalized intersections at red phase.…”

Cooperative Ecological Adaptive Cruise Control for Plug-In Hybrid Electric Vehicle Based on Approximate Dynamic Programming

“…Furthermore, some studies also plan velocity trajectories by considering traffic lights to avoid unnecessary start/stops [17]. A hierarchical optimization framework is proposed by considering the uncertainty and dynamic variation of waiting queue and is declared to decrease the fuel consumption by 4% [18]. As previously mentioned, the eco-driving control in single-vehicle scenarios mainly plans trajectories according to speed limits, terrains and traffic lights.…”