A Decision-Making Model of Left-Turn Behavior for Autonomous Vehicles at Urban Intersections Using a Single-Vehicle Scenario

2021 ◽  
Author(s):  
Xue-Mei Chen ◽  
Yang-Jia-Xin Ou ◽  
Zi-Jia Wang ◽  
Meng-Xi Li
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Left Turn ◽  
Single Vehicle ◽  
Decision Making Model

Implementation of Decision-Making Model System for Autonomous Vehicles in Virtual Environment

2006 ◽  
Vol 532-533 ◽  
pp. 1128-1131
Author(s):  
Yan Fei Liang ◽  
Han Wu He ◽  
De Tao Zheng ◽  
Xin Chen
Keyword(s):  
Decision Making ◽  
Virtual Environment ◽  
Autonomous Vehicles ◽  
Model System ◽  
Virtual Reality Environment ◽  
Environment Modeling ◽  
Driving Simulators ◽  
Modeling Technology ◽  
Virtual Vehicle ◽  
Decision Making Model

This paper established the framework of the decision-making model system for autonomous vehicles. Based on virtual reality environment modeling technology, the virtual scene was obtained. The driving performance of autonomous vehicles in real environment was simulated with that of the virtual vehicle in virtual environment. It was studied the influence of driver’s aggressiveness on lane-changed performance through considering human factors, and several longitudinal driving modes were classified and discussed. Three-power B spline function was used in this paper to plan path by interpolating characteristics points. The driving framework and the driving models described in this paper serve to address the problem of building more realistic traffic at the microscopic level in driving simulators. The autonomous vehicles based on this system can be used as the vehicles in simulators and help to design traffic or help to verify the performance of vehicles.

scholarly journals A Security-by-Design Decision-Making Model for Risk Management in Autonomous Vehicles

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Mohamed Abdel-Basset ◽  
Abduallah Gamal ◽  
Nour Moustafa ◽  
Ahmed Abdel-Monem ◽  
Nissreen El-Saber
Keyword(s):  
Decision Making ◽  
Risk Management ◽  
Autonomous Vehicles ◽  
Design Decision ◽  
Security By Design ◽  
Decision Making Model

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.

scholarly journals Communications and Decisions of Autonomous Vehicles in Virtual Environments

2010 ◽  
Vol 9 (2) ◽  
pp. 53-61
Author(s):  
Ronald R. R. ◽  
Zhishuai Yin ◽  
Yingzi Lin ◽  
Sagar Kamarthi
Keyword(s):  
Decision Making ◽  
Virtual Environments ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Local Level ◽  
Communication Model ◽  
Perception Model ◽  
Independent Entity ◽  
Autonomous Objects ◽  
Decision Making Model

In many virtual environments, autonomous objects, such as people and vehicles, are essential to increase the feeling of presence. A goal is to have autonomous objects behave as humans, or in the case of vehicles, as if humans controlled them. Such objects are known as intelligent autonomous objects. We present a combination of a communication model and a decisionmaking model to achieve the goal of modeling autonomous objects that behave intelligently. Both models are attached to autonomous objects that represent people and vehicles in a virtual environment. This enables such an autonomous object to be an independent entity that is self-motivated and self-controlled. These intelligent autonomous objects are able to communicate with other autonomous objects via their communication model according to decisions reached by their decision-making model. The decision-making model relies on the communication model to investigate possible outcomes before making decisions. The communication model defines senders, receivers, contents, and channels (media through which content is transferred) in realtime to gather desired information from specified objects. The decision-making model is divided into two levels, the global level and the local level. These work, respectively, with global information perceived by a perception model and the local information received by the communications model. A group of logic rules are formulated as decision trees to model the process of making decisions on the basis of real-time activities. We used traffic and people, in a virtual environments based driving simulator, as examples of intelligent communicating autonomous objects

scholarly journals Embedded Bayesian Network Contribution for a Safe Mission Planning of Autonomous Vehicles

Algorithms ◽  
2020 ◽  
Vol 13 (7) ◽  
pp. 155 ◽  
Author(s):  
Catherine Dezan ◽  
Sara Zermani ◽  
Chabha Hireche
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Probabilistic Models ◽  
Automatic Generation ◽  
Mission Planning ◽  
Pattern Design ◽  
Decision Network ◽  
Markov Decision ◽  
Speed Up ◽  
Decision Making Model

Bayesian Networks (BN) are probabilistic models that are commonly used for the diagnosis in numerous domains (medicine, finance, transport, robotics, …). In the case of autonomous vehicles, they can contribute to elaborate intelligent monitors that can take the environmental context into account. We show in this paper some main abilities of BN that can help in the elaboration of fault detection isolation and recovery (FDIR) modules. One of the main difficulty with the BN model is generally to elaborate these ones according to the case of study. Then, we propose some automatic generation techniques from failure mode and effects analysis (FMEA)-like tables using the pattern design approach. Once defined, these modules have to operate online for autonomous vehicles. In a second part, we propose a design methodology to embed the real-time and non-intrusive implementations of the BN modules using FPGA-SoC support. We show that the FPGA implementation can offer an interesting speed-up with very limited energy cost. Lastly, we show how these BN modules can be incorporated into the decision-making model for the mission planning of unmanned aerial vehicles (UAVs). We illustrate the integration by means of two models: the Decision Network model that is a straightforward extension of the BN model, and the BFM model that is an extension of the Markov Decision Process (MDP) decision-making model incorporating a BN. We illustrate the different proposals with realistic examples and show that the hybrid implementation on FPGA-SoC can offer some benefits.

scholarly journals Analysis of Pedestrian Street-Crossing Decision-Making Based on Vehicle Deceleration-Safety Gap

2020 ◽  
Vol 17 (24) ◽  
pp. 9247
Author(s):  
Hongjia Zhang ◽  
Yingshi Guo ◽  
Yunxing Chen ◽  
Qinyu Sun ◽  
Chang Wang
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Traffic Crashes ◽  
Motion Data ◽  
Street Crossing ◽  
Road Users ◽  
Vulnerable Road Users ◽  
Decision Making Model ◽  
Pedestrian Street ◽  
Vehicle Deceleration

Numerous traffic crashes occur every year on zebra crossings in China. Pedestrians are vulnerable road users who are usually injured severely or fatally during human-vehicle collisions. The development of an effective pedestrian street-crossing decision-making model is essential to improving pedestrian street-crossing safety. For this purpose, this paper carried out a naturalistic field experiment to collect a large number of vehicle and pedestrian motion data. Through interviewed with many pedestrians, it is found that they pay more attention to whether the driver can safely brake the vehicle before reaching the zebra crossing. Therefore, this work established a novel decision-making model based on the vehicle deceleration-safety gap (VD-SGM). The deceleration threshold of VD-SGM was determined based on signal detection theory (SDT). To verify the performance of VD-SGM proposed in this work, the model was compared with the Raff model. The results show that the VD-SGM performs better and the false alarm rate is lower. The VD-SGM proposed in this work is of great significance to improve pedestrians’ safety. Meanwhile, the model can also increase the efficiency of autonomous vehicles.

scholarly journals A Decision-Making Model for Autonomous Vehicles at Urban Intersections Based on Conflict Resolution

2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Zi-jia Wang ◽  
Xue-mei Chen ◽  
Pin Wang ◽  
Meng-xi Li ◽  
Yang-jia-xin Ou ◽  
...  
Keyword(s):  
Decision Making ◽  
Conflict Resolution ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Gaussian Process Regression ◽  
Nsga Ii ◽  
Policy Gradient ◽  
Depth Study ◽  
Tactical Decision ◽  
Decision Making Model

The decision-making models that are able to deal with complex and dynamic urban intersections are critical for the development of autonomous vehicles. A key challenge in operating autonomous vehicles robustly is to accurately detect the trajectories of other participants and to consider safety and efficiency concurrently into interactions between vehicles. In this work, we propose an approach for developing a tactical decision-making model for vehicles which is capable of predicting the trajectories of incoming vehicles and employs the conflict resolution theory to model vehicle interactions. The proposed algorithm can help autonomous vehicles cross intersections safely. Firstly, Gaussian process regression models were trained with the data collected at intersections using subgrade sensors and a retrofit autonomous vehicle to predict the trajectories of incoming vehicles. Then, we proposed a multiobjective optimization problem (MOP) decision-making model based on efficient conflict resolution theory at intersections. After that, a nondominated sorting genetic algorithm (NSGA-II) and deep deterministic policy gradient (DDPG) are employed to select the optimal motions in comparison with each other. Finally, a simulation and verification platform was built based on Matlab/Simulink and PreScan. The reliability and effectiveness of the tactical decision-making model was verified by simulations. The results indicate that DDPG is more reliable and effective than NSGA-II to solve the MOP model, which provides a theoretical basis for the in-depth study of decision-making in a complex and uncertain intersection environment.

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