Multiagent Cooperation for Decision-Making in the Car-Following Behavior

2016 ◽  
pp. 391-401 ◽  
Author(s):  
Anouer Bennajeh ◽  
Fahem Kebair ◽  
Lamjed Ben Said ◽  
Samir Aknine
Keyword(s):  
Decision Making ◽  
Car Following ◽  
Multiagent Cooperation

scholarly journals Human-like Decision Making and Motion Control for Smooth and Natural Car Following

2021 ◽  
pp. 1-1
Author(s):  
Chongfeng Wei ◽  
Evangelos Paschalidis ◽  
Natasha Merat ◽  
Albert Solernou ◽  
Foroogh Hajiseyedjavadi ◽  
...  
Keyword(s):  
Decision Making ◽  
Motion Control ◽  
Car Following

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.

Analysis Improtant Factors Influencing Driver Decision-Making Based on the Principal Component Method

2014 ◽  
Vol 988 ◽  
pp. 683-686
Author(s):  
Jia Yang Li ◽  
Qin Xue ◽  
Jia Xing Tong
Keyword(s):  
Decision Making ◽  
Principal Component ◽  
Intelligent Transportation ◽  
Principal Component Method ◽  
Car Following ◽  
Decision Factors ◽  
Theoretical Support ◽  
Factors Influencing ◽  
Dynamic Factors ◽  
Decision Making Model

In Intelligent Transportation microscopic study, driver's physical and psychological factors play an important role for driving decisions . Considered by static factors and dynamic factors, this paper establishes 22 the driver's car-following decision factors in hierarchy.We use principal component analysis gain that nine indicators play a crucial role in driving the decision-making.Conclusion in this article provides theoretical support for the establishment of the next drive decision-making model .

Driver's Speed Decision-Making Model Based on ANFIS

2014 ◽  
Vol 488-489 ◽  
pp. 955-960 ◽  
Author(s):  
Lian Li ◽  
Song Yang ◽  
Wen Jing Cao
Keyword(s):  
Decision Making ◽  
Fuzzy Inference ◽  
Model Simulation ◽  
Original Data ◽  
Vehicle Speed ◽  
Anfis Model ◽  
Inference System ◽  
Microscopic Traffic Simulation ◽  
Car Following ◽  
The Difference

To simulate the driver's ability to deal with uncertainty and solve the unsmooth problem in the driving-status-transformation between free-traveling and car-following during the microscopic traffic simulation, the Adaptive Neuro-Fuzzy Inference System (ANFIS) was introduced to model the driver's speed decision-making behavior which integrated the free-traveling and car-following behavior. The difference between velocity and desired speed was added into the inputs of the ANFIS model besides vehicle speed, relative distance and relative velocity which commonly appeared in car-following models. In this paper, the NGSIM (Next Generation Simulation) data was used to calibrate and evaluate the model. With the analysis and pretreatment of NGSIM data, drivers reaction time was calibrated, drivers were clustered into three categories according to the level of recklessness, and the desired speed of different driver characteristic in different vehicle was approximated as the corresponding free speed. Using the processed NGSIM data, the ANFIS model was trained and the model output was validated and compared with the original data. The results showed that the ANFIS model performed well. In addition, the output of ANFIS model under car-following state was compared with that of GM model. This comparison provided a better chance to analyze the performance of the model and showed that the model simulation the driving data in a more realistic way.

Research on decision-making of autonomous vehicle following based on reinforcement learning method

2019 ◽  
Vol 46 (3) ◽  
pp. 444-452 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Kelong Wang ◽  
Guotao Xie ◽  
Yuchao Liu
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Learning System ◽  
Explicit Calculation ◽  
Gradient Descent Method ◽  
Learning Method ◽  
Content Type ◽  
Car Following

Purpose Over the past decades, there has been significant research effort dedicated to the development of autonomous vehicles. The decision-making system, which is responsible for driving safety, is one of the most important technologies for autonomous vehicles. The purpose of this study is the use of an intensive learning method combined with car-following data by a driving simulator to obtain an explanatory learning following algorithm and establish an anthropomorphic car-following model. Design/methodology/approach This paper proposed car-following method based on reinforcement learning for autonomous vehicles decision-making. An approximator is used to approximate the value function by determining state space, action space and state transition relationship. A gradient descent method is used to solve the parameter. Findings The effect of car-following on certain driving styles is initially achieved through the simulation of step conditions. The effect of car-following initially proves that the reinforcement learning system is more adaptive to car following and that it has certain explanatory and stability based on the explicit calculation of R. Originality/value The simulation results show that the car-following method based on reinforcement learning for autonomous vehicle decision-making realizes reliable car-following decision-making and has the advantages of simple sample, small amount of data, simple algorithm and good robustness.

scholarly journals Driving Behavior Modeling Based on Consistent Variable Selection in a PWARX Model

2021 ◽  
Vol 11 (11) ◽  
pp. 4938
Author(s):  
Jude Chibuike Nwadiuto ◽  
Hiroyuki Okuda ◽  
Tatsuya Suzuki
Keyword(s):  
Decision Making ◽  
Variable Selection ◽  
Motion Control ◽  
Driving Behavior ◽  
Optimal Number ◽  
Behavior Modeling ◽  
Driver Model ◽  
Explanatory Variables ◽  
Car Following ◽  
Proposed Model

This paper proposes the hybrid system model identified by a PWARX (piecewise affine autoregressive exogenous) model for modeling human driving behavior. In the proposed model, the mode segmentation is carried out automatically and the optimal number of modes is decided by a novel methodology based on consistent variable selection. In addition, model flexibility is added within the ARX (autoregressive exogenous) partitions in the form of statistical variable selection. The proposed method is able to capture both the decision-making and motion-control facets of the driving behavior. The resulting model is an optimal basal model which is not affected by the choice of data, where the explanatory variables are allowed to vary within each ARX region, thus, allowing a higher-level understanding of the motion-control aspect of the driving behavior, as well as explaining the driver’s decision-making. The proposed model is applied to model the car-following driving task based on real-road driving data, as well as to ROS-CARLA-based car-following simulation and compared to Gipp’s driver model. Obtained results that show better performance both on prediction performance and mimicking actual real-road driving demonstrates and validates the usefulness of the model.

scholarly journals Bi-level Decision-making Modeling for an Autonomous Driver Agent: Application in the Car-following Driving Behavior

2019 ◽  
Vol 33 (13) ◽  
pp. 1157-1178 ◽  
Author(s):  
Anouer Bennajeh ◽  
Slim Bechikh ◽  
Lamjed Ben Said ◽  
Samir Aknine
Keyword(s):  
Decision Making ◽  
Driving Behavior ◽  
Car Following

scholarly journals Car-following method based on inverse reinforcement learning for autonomous vehicle decision-making

2018 ◽  
Vol 15 (6) ◽  
pp. 172988141881716 ◽  
Author(s):  
Hongbo Gao ◽  
Guanya Shi ◽  
Guotao Xie ◽  
Bo Cheng
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Optimization Problems ◽  
Learning Algorithm ◽  
Autonomous Vehicle ◽  
Sequential Decision ◽  
Inverse Reinforcement Learning ◽  
Car Following ◽  
Reward Function ◽  
Automatic Driving

There are still some problems need to be solved though there are a lot of achievements in the fields of automatic driving. One of those problems is the difficulty of designing a car-following decision-making system for complex traffic conditions. In recent years, reinforcement learning shows the potential in solving sequential decision optimization problems. In this article, we establish the reward function R of each driver data based on the inverse reinforcement learning algorithm, and r visualization is carried out, and then driving characteristics and following strategies are analyzed. At last, we show the efficiency of the proposed method by simulation in a highway environment.

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