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]  

A game-theoretic model for roadway performance management

2014 ◽  
Vol 4 (1) ◽  
pp. 40-54 ◽  
Author(s):  
Hesham Osman ◽  
Mazdak Nikbakht
Keyword(s):  
Asset Management ◽  
Decision Makers ◽  
Theoretic Model ◽  
Theoretic Approach ◽  
Political Decision ◽  
Content Type ◽  
Road Users ◽  
Game Theoretic ◽  
The Impact ◽  
Asset Managers

Purpose – The purpose of this paper is to present a socio-technical approach to modeling the behavior of roadway users, asset managers, and politicians toward roadway performance and asset management. This approach models the complex interactions that occur between these agents in a complex system. Most modeling approaches in the domain of infrastructure asset management take a purely asset-centric approach and fail to address these socio-technical interactions. Design/methodology/approach – Interactions among political decision makers, asset management strategy developers, and road users are modeled using a game-theoretic approach. The interactions are modeled as a non-cooperative game in which politicians, asset managers, and road users are the main players. Each player is autonomous and aims to come up with the set of moves to maximize their respective level of satisfaction in response to other players’ moves. Multi-attribute utility theory is used to deal with multitude of players’ goals, and the Nash equilibria of the game are south out to develop appropriate strategies for different players. Findings – An illustrative example for a road network of a Canadian city is used to demonstrate the developed methodology. The developed methodology demonstrates how behaviors of various agents involved in the sphere of asset management impacts their collective decision-making behavior. Originality/value – The developed framework provides asset managers and political decision makers with a valuable tool to evaluate the impact of public policy decisions related to asset managers on road performance and the overall satisfaction of road users.

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.

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.

Virtual Enterprise Coalition Strategy with Game Theoretic Multiagent Paradigm

2011 ◽  
pp. 263-282 ◽  
Author(s):  
Toshiya Kaihara ◽  
Susumu Fujii
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Virtual Enterprise ◽  
Basic Research ◽  
Negotiation Process ◽  
Computer Simulation Model ◽  
Theoretic Approach ◽  
Manufacturing Organizations ◽  
Game Theoretic ◽  
Game Theoretic Approach

Nowadays, virtual enterprise (VE) is a crucial paradigm of business management in an agile environment. VE exists in both service and manufacturing organizations, although the complexity of each enterprise in a VE may vary greatly from industry to industry. Obviously, there is a need for a mechanism through which these different functions can be integrated together transparently. In this contribution, we focus on the negotiation process in VE formulation as a basic research to clarify its effective management in terms of partner search. Each enterprise in VE is defined as an agent with multiutilities, and a framework of multiagent programming with game theoretic approach is newly proposed as a negotiation algorithm among the agents. Each unit is defined as an agent in our VE model, and their decision making is formulated as a game theoretic methodology. We develop a computer simulation model to form VEs through multiple negotiations among several potential members in the negotiation domain, and finally clarify the formulation dynamism with the negotiation process.

Scheduling Multi-Workflows Over Heterogeneous Virtual Machines With a Multi-Stage Dynamic Game-Theoretic Approach

2018 ◽  
Vol 15 (4) ◽  
pp. 82-96 ◽  
Author(s):  
Lei Wu ◽  
Yuandou Wang
Keyword(s):  
Virtual Machines ◽  
Dynamic Game ◽  
Scientific Workflow ◽  
Third Party ◽  
Theoretic Model ◽  
Theoretic Approach ◽  
Multi Stage ◽  
Game Theoretic ◽  
Heterogeneous Cloud ◽  
Cloud Infrastructures

Cloud computing, with dependable, consistent, pervasive, and inexpensive access to geographically distributed computational capabilities, is becoming an increasingly popular platform for the execution of scientific applications such as scientific workflows. Scheduling multiple workflows over cloud infrastructures and resources is well recognized to be NP-hard and thus critical to meeting various types of Quality-of-Service (QoS) requirements. In this work, the authors consider a multi-objective scientific workflow scheduling framework based on the dynamic game-theoretic model. It aims at reducing make-spans, cloud cost, while maximizing system fairness in terms of workload distribution among heterogeneous cloud virtual machines (VMs). The authors consider randomly-generated scientific workflow templates as test cases and carry out extensive real-world tests based on third-party commercial clouds. Experimental results show that their proposed framework outperforms traditional ones by achieving lower make-spans, lower cost, and better system fairness.

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