Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Hang, Peng; Lv, Chen; Huang, Chao; Hu, Zhongxu

doi:10.1109/tits.2020.3036984

Cited by 206 publications

(90 citation statements)

References 43 publications

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“…Decision making in autonomous vehicles (AVs) has been addressed in several works; for example, the authors in [ 10 ] developed an approach to mimic human behavior. A variety of driving styles were considered to study driving safety, ride comfort, and travel efficiency as utility functions.…”

Section: Related Workmentioning

confidence: 99%

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Smirnov

Liu

Validi

et al. 2021

Sensors

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Autonomous vehicles are expected to display human-like behavior, at least to the extent that their decisions can be intuitively understood by other road users. If this is not the case, the coexistence of manual and autonomous vehicles in a mixed environment might affect road user interactions negatively and might jeopardize road safety. To this end, it is highly important to design algorithms that are capable of analyzing human decision-making processes and of reproducing them. In this context, lane-change maneuvers have been studied extensively. However, not all potential scenarios have been considered, since most works have focused on highway rather than urban scenarios. We contribute to the field of research by investigating a particular urban traffic scenario in which an autonomous vehicle needs to determine the level of cooperation of the vehicles in the adjacent lane in order to proceed with a lane change. To this end, we present a game theory-based decision-making model for lane changing in congested urban intersections. The model takes as input driving-related parameters related to vehicles in the intersection before they come to a complete stop. We validated the model by relying on the Co-AutoSim simulator. We compared the prediction model outcomes with actual participant decisions, i.e., whether they allowed the autonomous vehicle to drive in front of them. The results are promising, with the prediction accuracy being 100% in all of the cases in which the participants allowed the lane change and 83.3% in the other cases. The false predictions were due to delays in resuming driving after the traffic light turned green.

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Section: Related Workmentioning

confidence: 99%

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Smirnov

Liu

Validi

et al. 2021

Sensors

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“…Potential field is an effective method to model the dynamics of the vehicle and describe its interaction with surrounding obstacles. In [88], a human-like decision making framework for autonomous vehicles is proposed, where the potential field method and MPC are used to plan the collision-avoidance path and provide predicted motion states for the human-like decision making module. An integrated framework to deal with the decision making and motion planning is presented in [89] for lanechange maneuvers, while considering social behaviors of surrounding traffic participants.…”

Section: Motion Planning By Mpc For a Single Agvmentioning

confidence: 99%

Model predictive control for autonomous ground vehicles: a review

Hirche

Huang

et al. 2021

Auton. Intell. Syst.

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This paper reviews model predictive control (MPC) and its wide applications to both single and multiple autonomous ground vehicles (AGVs). On one hand, MPC is a well-established optimal control method, which uses the predicted future information to optimize the control actions while explicitly considering constraints. On the other hand, AGVs are able to make forecasts and adapt their decisions in uncertain environments. Therefore, because of the nature of MPC and the requirements of AGVs, it is intuitive to apply MPC algorithms to AGVs. AGVs are interesting not only for considering them alone, which requires centralized control approaches, but also as groups of AGVs that interact and communicate with each other and have their own controller onboard. This calls for distributed control solutions. First, a short introduction into the basic theoretical background of centralized and distributed MPC is given. Then, it comprehensively reviews MPC applications for both single and multiple AGVs. Finally, the paper highlights existing issues and future research directions, which will promote the development of MPC schemes with high performance in AGVs.

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“…Many advanced algorithms have been proposed to overcome the defects of the sensors [ 16 , 17 ]. However, these algorithms are always limited to particular scenarios, e.g., lane changing [ 18 ] and turning [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles

Wang

Chen

Jin

et al. 2021

Sensors

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The collision warning system (CWS) plays an essential role in vehicle active safety. However, traditional distance-measuring solutions, e.g., millimeter-wave radars, ultrasonic radars, and lidars, fail to reflect vehicles’ relative attitude and motion trends. In this paper, we proposed a vehicle-to-vehicle (V2V) cooperative collision warning system (CCWS) consisting of an ultra-wideband (UWB) relative positioning/directing module and a dead reckoning (DR) module with wheel-speed sensors. Each vehicle has four UWB modules on the body corners and two wheel-speed sensors on the rear wheels in the presented configuration. An over-constrained localization method is proposed to calculate the relative position and orientation with the UWB data more accurately. Vehicle velocities and yaw rates are measured by wheel-speed sensors. An extended Kalman filter (EKF) is applied based on the relative kinematic model to combine the UWB and DR data. Finally, the time to collision (TTC) is estimated based on the predicted vehicle collision position. Furthermore, through UWB signals, vehicles can simultaneously communicate with each other and share information, e.g., velocity, yaw rate, which brings the potential for enhanced real-time performance. Simulation and experimental results show that the proposed method significantly improves the positioning, directing, and velocity estimating accuracy, and the proposed system can efficiently provide collision warning.

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Human-Like Decision Making for Autonomous Driving: A Noncooperative Game Theoretic Approach

Cited by 206 publications

References 43 publications

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

A Game Theory-Based Approach for Modeling Autonomous Vehicle Behavior in Congested, Urban Lane-Changing Scenarios

Model predictive control for autonomous ground vehicles: a review

A Novel V2V Cooperative Collision Warning System Using UWB/DR for Intelligent Vehicles

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