scholarly journals Adaptive Game-Theoretic Decision Making for Autonomous Vehicle Control at Roundabouts

2018 ◽  
Author(s):  
Ran Tian ◽  
Sisi Li ◽  
Nan Li ◽  
Ilya Kolmanovsky ◽  
Anouck Girard ◽  
...  
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Game Theoretic

scholarly journals Human-like Interactive Behavior Generation for Autonomous Vehicles: A Bayesian Game-theoretic Approach with Turing Test

2021 ◽  
Author(s):  
Yiran Zhang ◽  
Peng Hang ◽  
Chao Huang ◽  
Chen Lv
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Turing Test ◽  
Theoretic Model ◽  
Theoretic Approach ◽  
Bayesian Game ◽  
Vehicle Simulation ◽  
Road Users ◽  
Game Theoretic

Interacting with surrounding road users is a key feature of vehicles and is critical for intelligence testing of autonomous vehicles. The Existing interaction modalities in autonomous vehicle simulation and testing are not sufficiently smart and can hardly reflect human-like behaviors in real world driving scenarios. To further improve the technology, in this work we present a novel hierarchical game-theoretical framework to represent naturalistic multi-modal interactions among road users in simulation and testing, which is then validated by the Turing test. Given that human drivers have no access to the complete information of the surrounding road users, the Bayesian game theory is utilized to model the decision-making process. Then, a probing behavior is generated by the proposed game theoretic model, and is further applied to control the vehicle via Markov chain. To validate the feasibility and effectiveness, the proposed method is tested through a series of experiments and compared with existing approaches. In addition, Turing tests are conducted to quantify the human-likeness of the proposed algorithm. The experiment results show that the proposed Bayesian game theoretic framework can effectively generate representative scenes of human-like decision-making during autonomous vehicle interactions, demonstrating its feasibility and effectiveness. Corresponding author(s) Email:   [email protected]  

scholarly journals Game Theoretic Modeling of Driver and Vehicle Interactions for Verification and Validation of Autonomous Vehicle Control Systems

2018 ◽  
Vol 26 (5) ◽  
pp. 1782-1797 ◽  
Author(s):  
Nan Li ◽  
Dave W. Oyler ◽  
Mengxuan Zhang ◽  
Yildiray Yildiz ◽  
Ilya Kolmanovsky ◽  
...  
Keyword(s):  
Control Systems ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Verification And Validation ◽  
Game Theoretic

scholarly journals The Extent and Use of Artificial Intelligence to Achieve the Big Four Agenda in Kenya

2020 ◽  
Vol 1 (1) ◽  
pp. 1-7
Author(s):  
Mgala Mvurya
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Autonomous Vehicle ◽  
Vehicle Control ◽  
Human Intelligence ◽  
Guided Surgery ◽  
Development Agenda ◽  
Developed World ◽  
Big Four ◽  
Technology Trend

Artificial Intelligence (AI) is the human-like intelligence given to systems and demonstrated by machines in doing tasks associated with human intelligence. AI systems can therefore, think like humans, act like humans, think rationally or act rationally. AI systems are not only more powerful and more useful than ordinary computers, but they also solve complex emerging human problems. These systems can be used in areas such as medicine, to conduct guided surgery; transportation, for autonomous vehicle control; face recognition; speech recognition; decision making in agriculture, manufacturing and housing. The present study is a review of the AI technology trend. A search was conducted on literature, technology magazines, and other internet sources. A discussion is presented on how AI systems have been widely applied in the developed world and the extent these systems are being embraced in other countries like China to spar development. Thereafter, this paper discusses the extent to which AI has been applied in the specific areas that relate to the Kenyan Big Four Agenda. The contribution of this paper is a framework that the Kenya Government can adapt in the application AI’s world transforming technologies to achieve its development agenda. We discuss the challenges that hinder full application of the AI technology and present the way forward.

scholarly journals Hybrid Verification Technique for Decision-Making of Self-Driving Vehicles

2021 ◽  
Vol 10 (3) ◽  
pp. 42
Author(s):  
Mohammed Al-Nuaimi ◽  
Sapto Wibowo ◽  
Hongyang Qu ◽  
Jonathan Aitken ◽  
Sandor Veres
Keyword(s):  
Decision Making ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Practical Implementation ◽  
Model Checker ◽  
Multi Agent Systems ◽  
Time Operation ◽  
Bdi Agent ◽  
Perception System ◽  
Probability Of Success

The evolution of driving technology has recently progressed from active safety features and ADAS systems to fully sensor-guided autonomous driving. Bringing such a vehicle to market requires not only simulation and testing but formal verification to account for all possible traffic scenarios. A new verification approach, which combines the use of two well-known model checkers: model checker for multi-agent systems (MCMAS) and probabilistic model checker (PRISM), is presented for this purpose. The overall structure of our autonomous vehicle (AV) system consists of: (1) A perception system of sensors that feeds data into (2) a rational agent (RA) based on a belief–desire–intention (BDI) architecture, which uses a model of the environment and is connected to the RA for verification of decision-making, and (3) a feedback control systems for following a self-planned path. MCMAS is used to check the consistency and stability of the BDI agent logic during design-time. PRISM is used to provide the RA with the probability of success while it decides to take action during run-time operation. This allows the RA to select movements of the highest probability of success from several generated alternatives. This framework has been tested on a new AV software platform built using the robot operating system (ROS) and virtual reality (VR) Gazebo Simulator. It also includes a parking lot scenario to test the feasibility of this approach in a realistic environment. A practical implementation of the AV system was also carried out on the experimental testbed.

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.

Sign in / Sign up

Export Citation Format

Share Document