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

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1523
Author(s):  
Nikita Smirnov ◽  
Yuzhou Liu ◽  
Aso Validi ◽  
Walter Morales-Alvarez ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Traffic ◽  
Road User ◽  
Lane Change ◽  
Traffic Light ◽  
User Interactions ◽  
Lane Changing

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.

Multi-Player Dynamic Game-Based Automatic Lane-Changing Decision Model under Mixed Autonomous Vehicle and Human-Driven Vehicle Environment

2020 ◽  
Vol 2674 (11) ◽  
pp. 165-183
Author(s):  
Yuewen Yu ◽  
Shikun Liu ◽  
Peter J. Jin ◽  
Xia Luo ◽  
Mengxue Wang
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Splitting Method ◽  
Dynamic Game ◽  
Cruise Control ◽  
Lane Changing ◽  
Bicycle Model ◽  
Decision Making Model

The lane-changing decision-making process is challenging but critical to ensure safe and smooth maneuvers for autonomous vehicles (AVs). Conventional Gipps-type algorithms lack the flexibility for practical use under a mixed autonomous vehicle and human-driven vehicle (AV-HV) environment. Algorithms based on utility ignore the reactions of surrounding vehicles to the lane-changing vehicle. Game theory is a good way to solve the shortcomings of current algorithms, but most models based on game theory simplify the game with surrounding vehicles to the game with the following vehicle in the target lane, which means that the lane-changing decision under a mixed environment is not realized. This paper proposes a lane-changing decision-making model which is suitable for an AV to change lanes under a mixed environment based on a multi-player dynamic game theory. The overtaking expectation parameter (OEP) is introduced to estimate the utility of the following vehicle, OEP can be calculated by the proposed non-lane-based full velocity difference model with the consideration of lateral move and aggressiveness. This paper further proposes a hybrid splitting method algorithm to obtain the Nash equilibrium solution in the multi-player game to obtain the optimal strategy of lane-changing decision for AVs. An adaptive cruise control simulation environment is developed with MATLAB’s Simulink toolbox using Next Generation Simulation (NGSIM) data as the background traffic flow. The classic bicycle model is used in the control of involved HVs. Simulation results show the efficiency of the proposed multi-player dynamic game-based algorithm for lane-changing decision making by AVs under a mixed AV-HV environment.

Lane Change Decision Analysis Based on Drivers' Perception-Judgment and Game Theory

2013 ◽  
Vol 361-363 ◽  
pp. 1875-1879 ◽  
Author(s):  
Jin Shuan Peng ◽  
Ying Shi Guo ◽  
Yi Ming Shao
Keyword(s):  
Game Theory ◽  
Decision Making ◽  
Nash Equilibrium ◽  
Equilibrium Solution ◽  
Information Source ◽  
Driving Safety ◽  
External Information ◽  
Lane Change ◽  
Safety Level ◽  
Lane Changing

To clearly understand the mechanism of drivers lane-changing decision, based on drivers perception of external information, integrated cognitive judgment and game theory, the decision-making model was established, then the structure and operating mechanism of the model were detailedly analyzed. By introducing game theory-related knowledge, the non-cooperative mixed strategy game between the object vehicle and the following vehicle in the target lane was further discussed. Then, the benefits and Nash equilibrium solution of the participants in the game were deeply researched. Analysis shows that lane-changing decision is composed of information perception and three judgment-decision processes, the factors which would affect decision-making level include information source characteristics, the ability of drivers perception and comprehensive cognitive judgment, driving behavior characteristics and so on. The Nash equilibrium solution of the lane change game is determined by driving safety level, journey time and importance degree of the revenues.

An IMM-based POMDP decision algorithm using collision-risk function in mandatory lane change

2021 ◽  
pp. 095440702110397
Author(s):  
Yunfeng Huang ◽  
Wanzhong Zhao ◽  
Can Xu ◽  
Songchun Zou ◽  
Han Zhang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Risk Function ◽  
Interacting Multiple Model ◽  
Lane Change ◽  
Multiple Model ◽  
Collision Risk ◽  
Lane Changing ◽  
Decision Algorithm ◽  
Risk Environments

In order to make safe and reasonable decisions in some high-risk environments such as the mandatory lane change, we propose an IMM-based partially observable Markov decision process (POMDP) decision algorithm using the collision-risk function which combines the time-to-collision (TTC), the intervehicular time (IT), and the collision function for mandatory lane change. The newly proposed collision-risk function contains two parts: the vehicle impact factor and the collision function, which is used to assess the risk and determines whether the autonomous vehicle collides with surrounding vehicles. The IMM-base POMDP is used for decision-making and we apply the Monte Carlo Tree Search (MCTS) to solve the problem. In the decision-making process, the belief state is obtained by the Interacting Multiple Model (IMM) algorithm. With the collision-risk function and the probability distribution of the states of surrounding vehicles in the future, the proposed POMDP decision algorithm can determine whether the autonomous vehicle accelerates lane changing or decelerates lane changing, and obtain the acceleration corresponding to each path point. Finally, in order to verify the effectiveness of the algorithm, we perform a driver-in-the-loop simulation through Prescan. We use aggressive driver and conservative driver to control the rear vehicle of the target lane, respectively. Simulation results show that the proposed algorithm can accurately predict the accelerations of surrounding vehicles and make safe and reasonable decisions under two scenarios, which is superior to the general POMDP.

scholarly journals Research prospect of autonomous driving decision technology under complex traffic scenarios

2022 ◽  
Vol 355 ◽  
pp. 03031
Author(s):  
Yaoguang Cao ◽  
Yuyi Chen ◽  
Lu Liu
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Urban Traffic ◽  
Knowledge Map ◽  
Autonomous Decision ◽  
Decision Making System ◽  
Research Prospect ◽  
Safety And Stability

Decision-making system is the essential part of the autonomous vehicle “brain”, which determines the safety and stability of vehicles, and is also the key to reflect the intelligent level of autonomous vehicles. Compared with simple scenarios such as expressway, urban traffic scenarios have the characteristics of complex and frequent interaction between traffic participants. Carrying out in-depth research on complex traffic scenarios and optimizing autonomous decision-making algorithms are the key methods for the purpose of promoting the application of autonomous driving technologies. In the future, we can further combine the artificial intelligence methods such as cognitive or knowledge map, behaviour prediction of traffic participants, and humanoid intelligence, so as to enhance the intelligent level of autonomous driving.

Pedestrians, Autonomous Vehicles, and Cities

2016 ◽  
Vol 38 (1) ◽  
pp. 6-12 ◽  
Author(s):  
Adam Millard-Ball
Keyword(s):  
Travel Behavior ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Urban Neighborhoods ◽  
Urban Traffic ◽  
Risk Averse ◽  
Strategic Interactions ◽  
Road Users ◽  
Self Driving Cars ◽  
Time Autonomous

Autonomous vehicles, popularly known as self-driving cars, have the potential to transform travel behavior. However, existing analyses have ignored strategic interactions with other road users. In this article, I use game theory to analyze the interactions between pedestrians and autonomous vehicles, with a focus on yielding at crosswalks. Because autonomous vehicles will be risk-averse, the model suggests that pedestrians will be able to behave with impunity, and autonomous vehicles may facilitate a shift toward pedestrian-oriented urban neighborhoods. At the same time, autonomous vehicle adoption may be hampered by their strategic disadvantage that slows them down in urban traffic.

scholarly journals Decentralized Cooperative Lane-Changing Decision-Making for Connected Autonomous Vehicles*

IEEE Access ◽  
2016 ◽  
Vol 4 ◽  
pp. 9413-9420 ◽  
Author(s):  
Jianqiang Nie ◽  
Jian Zhang ◽  
Wanting Ding ◽  
Xia Wan ◽  
Xiaoxuan Chen ◽  
...  
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Lane Changing

Development of Operation Guidelines for Leader–Follower Autonomous Maintenance Vehicles at Work Zone Locations

2021 ◽  
pp. 036119812110566
Author(s):  
Qing Tang ◽  
Xianbiao Hu ◽  
Ruwen Qin
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Work Zone ◽  
Lane Changing ◽  
The Public ◽  
Car Following ◽  
Technical Requirements ◽  
Car Following Model ◽  
Niche Area

The rapid advancement of connected and autonomous vehicle (CAV) technologies, although possibly years away from wide application to the general public travel, are receiving attention from many state Departments of Transportation (DOT) in the niche area of using autonomous maintenance technology (AMT) to reduce fatalities of DOT workers in work zone locations. Although promising results are shown in testing and deployments in several states, current autonomous truck mounted attenuator (ATMA) system operators are not provided with much practical driving guidance on how to drive these new vehicle systems in a way that is safe to both the public and themselves. To this end, this manuscript aims to model and develop a set of rules and instructions for ATMA system operators, particularly when it comes to critical locations where essential decision making is needed. Specifically, three technical requirements are investigated: car-following distance, critical lane-changing gap distance, and intersection clearance time. Newell’s simplified car-following model, and the classic lane-changing behavior model are modified, with roll-ahead distance taken into account, to model the driving behaviors of the ATMA vehicles at those critical decision-making locations. Data are collected from real-world field testing to calibrate and validate the developed models. The modeling outputs suggest important thresholds for ATMA system operators to follow. For example, on a freeway with a speed limit of 70 mph and ATMA operating speed of 10 mph, car-following distance should be no less than 75 ft for the lead truck and 100 ft for the follower truck, the critical lane-changing gap distance is 912 ft, and a minimum intersection clearance is 15 s, which are all much higher than the requirements for a general vehicle.

scholarly journals Lateral control of an autonomous vehicle using integrated backstepping and sliding mode controller

2018 ◽  
Vol 233 (1) ◽  
pp. 141-151 ◽  
Author(s):  
Armin Norouzi ◽  
Milad Masoumi ◽  
Ali Barari ◽  
Saina Farrokhpour Sani
Keyword(s):  
Optimization Algorithm ◽  
Sliding Mode ◽  
Autonomous Vehicle ◽  
Lane Change ◽  
Lateral Control ◽  
Lane Changing ◽  
Dynamic Constraints ◽  
Bicycle Model ◽  
Double Lane Change ◽  
Backstepping Controller

In this paper, a novel Lyapunov-based robust controller by using meta-heuristic optimization algorithm has been proposed for lateral control of an autonomous vehicle. In the first step, double lane change path has been designed using a fifth-degree polynomial (quantic) function and dynamic constraints. A lane changing path planning method has been used to design the double lane change manoeuvre. In the next step, position and orientation errors have been extracted based on the two-degree-of-freedom vehicle bicycle model. A combination of sliding mode and backstepping controllers has been used to control the steering in this paper. Overall stability of the combined controller has been analytically proved by defining a Lyapunov function and based on Lyapunov stability theorem. The proposed controller includes some constant parameters which have effects on controller performance; therefore, particle swarm optimization algorithm has been used for finding optimum values of these parameters. The comparing result of the proposed controller with backstepping controller illustrated the better performance of the proposed controller, especially in the low road frictions. Simulation of designed controllers has been conducted by linking CarSim software with Matlab/Simulink which provides a nonlinear full vehicle model. The simulation was performed for manoeuvres with different durations and road frictions. The proposed controller has outperformed the backstepping controller, especially in low frictions.

scholarly journals End-to-End Automated Lane-Change Maneuvering Considering Driving Style Using a Deep Deterministic Policy Gradient Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5443
Author(s):  
Hongyu Hu ◽  
Ziyang Lu ◽  
Qi Wang ◽  
Chengyuan Zheng
Keyword(s):  
Autonomous Vehicles ◽  
The State ◽  
Steering Wheel ◽  
Lane Change ◽  
Simulation Environment ◽  
Longitudinal Control ◽  
Driving Style ◽  
Lane Changing ◽  
Policy Gradient ◽  
End To End

Changing lanes while driving requires coordinating the lateral and longitudinal controls of a vehicle, considering its running state and the surrounding environment. Although the existing rule-based automated lane-changing method is simple, it is unsuitable for unpredictable scenarios encountered in practice. Therefore, using a deep deterministic policy gradient (DDPG) algorithm, we propose an end-to-end method for automated lane changing based on lidar data. The distance state information of the lane boundary and the surrounding vehicles obtained by the agent in a simulation environment is denoted as the state space for an automated lane-change problem based on reinforcement learning. The steering wheel angle and longitudinal acceleration are used as the action space, and both the state and action spaces are continuous. In terms of the reward function, avoiding collision and setting different expected lane-changing distances that represent different driving styles are considered for security, and the angular velocity of the steering wheel and jerk are considered for comfort. The minimum speed limit for lane changing and the control of the agent for a quick lane change are considered for efficiency. For a one-way two-lane road, a visual simulation environment scene is constructed using Pyglet. By comparing the lane-changing process tracks of two driving styles in a simplified traffic flow scene, we study the influence of driving style on the lane-changing process and lane-changing time. Through the training and adjustment of the combined lateral and longitudinal control of autonomous vehicles with different driving styles in complex traffic scenes, the vehicles could complete a series of driving tasks while considering driving-style differences. The experimental results show that autonomous vehicles can reflect the differences in the driving styles at the time of lane change at the same speed. Under the combined lateral and longitudinal control, the autonomous vehicles exhibit good robustness to different speeds and traffic density in different road sections. Thus, autonomous vehicles trained using the proposed method can learn an automated lane-changing policy while considering safety, comfort, and efficiency.

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