Coordinated decisions of discretionary lane change between connected and automated vehicles on freeways: a game theory‐based lane change strategy

Abstract

Discretionary lane change is an essential part of connected and automated vehicles (CAVs) on freeway segments. Most existing studies were conducted to optimise the individual decision of discretionary lane change of CAVs. However, the effects of motion states and discretionary lane change decisions from the surrounding vehicles via vehicle to vehicle communication were ignored. To address such a problem, a game theory‐based lane change strategy is proposed to collaborate and optimise decisions of discretionary lane change between the CAVs. The payoff functions are formulated for three types of decision games and the payoff of each decision is quantitatively calculated considering the state information of surrounding vehicles. The Nash equilibrium is applied to find the optimal decision set for players. A simulation platform of a CAV environment built is used to conduct the simulation experiments. Various metrics are employed to evaluate the proposed strategy, such as total travel delay, surrogate safety measurement and wave number. The results show that the proposed lane change strategy using a game‐theoretical approach can effectively improve traffic operation, safety and oscillations compared to the baseline strategy. The proposed lane change strategy can further benefit the implementations of the CAVs.

Multi-agent reinforcement learning for safe lane changes by connected and autonomous vehicles: A survey

“…DLCs often result in increased speed for the ego vehicles, and they may have various positive impacts on the traffic at the macroscopic level, such as increasing road capacity, increasing traffic throughput, minimising traffic jam propagation, etc. DLCs focus primarily on safety and achieving macroscopic objectives like increased driving comfort, mobility, or throughput [22,28,29].…”

Characteristic Analysis and Decision Model of Lane-Changing Game for Intelligent Connected Vehicles

“…Sun et al [17] proposed a two-lane collaborative lane-change model by analyzing the impact of lane-change behavior on the target lane, and the experimental results showed that the method can effectively reduce the oscillation of traffic flow. Zheng et al [18] proposed a game model for optimizing the decision of lane-change vehicles by considering the influence of surrounding vehicle motion state and vehicle-vehicle communication, which can effectively improve traffic operation efficiency and reduce traffic oscillations. Liu et al [19] proposed a cooperative LPV/MPC and risk assessment cooperative driving strategy, and the results showed that the algorithm can effectively assist drivers in reducing vehicle risk.…”

Research on the vehicle lane-changing decision-making system in complex traffic environments

“…With the development of society, there is a wide range of research on automatic driving technology. Dang builds a lane-changing warning system by analyzing safe distances between vehicles [1]; Wang establishes an active lane-changing model for smart grid-connected vehicles based on the traditional braking model caused by minimizing lane-changing and the minimum safety distance model [2]; Fei proposes a safe active lane-changing model, which obtains information about the movement of the lane-changing car and the surrounding cars through Telematics, and outputs a safe and feasible optimal target lane [3]; Ji simulates lane highway traffic scenarios in order to improve longitudinal following stability and lateral safety, and to prevent vehicle instability, which improves the stability of vehicles traveling in the combination of following and lane-changing scenarios [4]. The above findings based on research in a connected vehicle environment focus only on individual lane-changing vehicles.…”