Study on Speed Planning of Signalized Intersections with Autonomous Vehicles Considering Regenerative Braking

Abstract

In order to reduce the energy consumption caused by the frequent braking of vehicles at signalized intersections, an optimized speed trajectory control method is proposed, based on braking energy recovery efficiency (BERE) in connection with an automated system for vehicle real-time interaction with roadside facilities and regional central control. Our objectives were as follows; firstly, to establish the simulation model of the hybrid energy regenerative braking system (HERBS) and to verify it by bench test. Secondly, to build up the genetic algorithm (GA) optimization model for the deceleration stopping of the HERBS. Then, to obtain signal light status and timing information to be the constraints; the BERE is to be the optimized objective, resulting in optimization for the speed trajectory under the deceleration stopping condition of a single signalized intersection. Finally, vehicle simulations in ADVISOR software are utilized to validate the optimization results. The results show that the BERE during deceleration stopping at a single signalized intersection after the speed trajectory optimization is 36.21% higher than that of inexperienced drivers, and 7.82% higher than that of experienced drivers.

Research on Multi-Objective Optimization Models for Intersection Crossing of Connected Autonomous Vehicles With Traffic Signals

“…The model's reward function is defined in equation ( 30) - (33): The constructed RL solution framework is depicted in Figure 14.…”

“…In previous research conducted by our research group, Yang et al [33] applied the genetic algorithm (GA) to solve the optimal braking trajectory problem for hybrid energy electric vehicles' (HEEVs) RBS in a V2X communication environment. Building on this, the current study aims to design a multi-objective optimization controller for CAVs passing through traffic light intersections in a V2X setting.…”

“…This is the author's version which has not been fully edited and content may change prior to final publication. Citation information: DOI 10.1109/ACCESS.2024.3374041 VOLUME XX, 2023 𝑅 = 𝛽 1 𝑅 1 + 𝛽 2 𝑅 2 + 𝛽 3 𝑅 3(33) where 𝛽 1 = 50 , 𝛽 2 = 30 , and 𝛽 3 = 20 are the weight coefficients for the three different reward values. 𝑆𝑂𝐶 𝑡 and 𝑆𝑂𝐶 𝑡−1 represent the current and previous states of the vehicle's state of charge.…”

Analysis and Comparative Study of Signalized and Unsignalized Intersection Operations and Energy-Emission Characteristics Based on Real Vehicle Data

“…For instance, Ren et al [16] developed the CVIS intersection control model based on traffic efficiency and vehicle energy consumption. Li et al [17] proposed an optimized speed-trajectory control method based on braking energy recovery efficiency to reduce energy consumption caused by frequent braking at signalized intersections. Wen et al [18] proposed an optimal control method for connected and automated vehicles to pass through signalized intersections, aiming to reduce energy consumption in mixed traffic scenarios with conventional vehicles and connected and automated vehicles.…”