Eco-Driving Decision Making Based on V2X Communication and Spatio-Temporal Prediction of Pedestrians

The operational dynamics of vehicular transportation significantly influence energy expenditure and contribute to the escalation of global warming. However, a noticeable gap exists in the availability of Eco-driving methodologies tailored to mitigate conflicts between pedestrians and vehicles. In response, this study proposes a Vehicle-to-Pedestrian communication Eco-driving (V2P Eco-driving) strategy that operates without traffic lights and incorporates collaborative pedestrian trajectory prediction. Its performance is evaluated through a comparative study with the Ecological Intelligent Traffic Lights System (Eco-ITLS) strategy, which adjusts traffic light phases based on pedestrian and vehicle flow detection. To enhance the generalization of the prediction model, pedestrian social interactions are modeled using relative displacement and velocity metrics, while Kalman filtering mitigates systemic discrepancies in vehicular and infrastructural components. A modified distance-discrete dynamic programming (D-DDP) algorithm, accounting for remaining travel time, is introduced to optimize eco-friendly vehicle actions. The algorithm is benchmarked against other Eco-driving algorithms in terms of solution quality, memory consumption, and computational efficiency. Experimental results demonstrate that the proposed model achieves a balance between computational efficiency and solution quality. Real-world data validation and parameter calibration confirm its practicality. Simulations further highlight the V2P Eco-driving strategy’s significant potential for reducing energy consumption and emissions compared to conventional traffic light-based Eco-driving strategies.