scholarly journals Effects of connected and autonomous vehicle merging behavior on mainline human-driven vehicle

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Lishengsa Yue ◽  
Mohamed Abdel-Aty ◽  
Zijin Wang
Keyword(s):  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Field Testing ◽  
Driving Safety ◽  
Reference Trajectory ◽  
Content Type ◽  
Merging Algorithm ◽  
Vehicle Platoon ◽  
Merging Behavior ◽  
Merging Algorithms

Purpose This study aims to evaluate the influence of connected and autonomous vehicle (CAV) merging algorithms on the driver behavior of human-driven vehicles on the mainline. Design/methodology/approach Previous studies designed their merging algorithms mostly based on either the simulation or the restricted field testing, which lacks consideration of realistic driving behaviors in the merging scenario. This study developed a multi-driver simulator system to embed realistic driving behavior in the validation of merging algorithms. Findings Four types of CAV merging algorithms were evaluated regarding their influences on driving safety and driving comfort of the mainline vehicle platoon. The results revealed significant variation of the algorithm influences. Specifically, the results show that the reference-trajectory-based merging algorithm may outperform the social-psychology-based merging algorithm which only considers the ramp vehicles. Originality/value To the best of the authors’ knowledge, this is the first time to evaluate a CAV control algorithm considering realistic driver interactions rather than by the simulation. To achieve the research purpose, a novel multi-driver driving simulator was developed, which enables multi-drivers to simultaneously interact with each other during a virtual driving test. The results are expected to have practical implications for further improvement of the CAV merging algorithm.

Human-vehicle dynamic model with driver’s neuromuscular characteristic for shared control of autonomous vehicle

2020 ◽  
pp. 095440702097710
Author(s):  
Wei Hanbing ◽  
Wu Yanhong ◽  
Chen Xing ◽  
Xu Jin ◽  
Rahul Sharma
Keyword(s):  
Driving Simulator ◽  
Unscented Kalman Filter ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Least Square ◽  
Shared Control ◽  
Driving Safety ◽  
Dynamic State ◽  
Commercial Application ◽  
Steering Torque

Over a long period of time, the fully autonomous vehicle is far from commercial application. The concept of ‘human-vehicle shared control (HVSC)’ provides a promising solution to enhance autonomous driving safety. In order to characterize the evolution of the driver’s feature in the process of HVSC, a dynamics model of HVSC with the driver’s neuromuscular characteristic is proposed in this paper. It takes into account the driver’s neuromuscular characteristics, such as stretch reflection, feedback stiffness, etc. By designing a model predictive control (MPC) controller, the feedback of the vehicle’s state and steering torque is constructed. For validation of the model, driving simulation has been conducted in our table-based driving simulator. The vehicle state and the surface electromyography of the driver’s arm working muscle group are collected simultaneously. Subsequently, the hierarchical least square (HLS) parameter identification and unscented Kalman filter (UKF) observer is used to identify and estimate the important characteristic parameters respectively based on the experimental results. The comparisons show that the HVSC can characterize the vehicle’s dynamic state and the driver’s personalized characteristic can be identified by HLS. This paper will serve as a theoretical basis of control strategy allocation between the human and vehicle during shared control for L3 class autonomous vehicle.

scholarly journals Intention-Aware Autonomous Driving Decision-Making in an Uncontrolled Intersection

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Weilong Song ◽  
Guangming Xiong ◽  
Huiyan Chen
Keyword(s):  
Decision Making ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Transition Process ◽  
Autonomous Driving ◽  
Driving Safety ◽  
Time Step ◽  
Reward Function ◽  
Pass Through

Autonomous vehicles need to perform social accepted behaviors in complex urban scenarios including human-driven vehicles with uncertain intentions. This leads to many difficult decision-making problems, such as deciding a lane change maneuver and generating policies to pass through intersections. In this paper, we propose an intention-aware decision-making algorithm to solve this challenging problem in an uncontrolled intersection scenario. In order to consider uncertain intentions, we first develop a continuous hidden Markov model to predict both the high-level motion intention (e.g., turn right, turn left, and go straight) and the low level interaction intentions (e.g., yield status for related vehicles). Then a partially observable Markov decision process (POMDP) is built to model the general decision-making framework. Due to the difficulty in solving POMDP, we use proper assumptions and approximations to simplify this problem. A human-like policy generation mechanism is used to generate the possible candidates. Human-driven vehicles’ future motion model is proposed to be applied in state transition process and the intention is updated during each prediction time step. The reward function, which considers the driving safety, traffic laws, time efficiency, and so forth, is designed to calculate the optimal policy. Finally, our method is evaluated in simulation with PreScan software and a driving simulator. The experiments show that our method could lead autonomous vehicle to pass through uncontrolled intersections safely and efficiently.

scholarly journals Performance and Robustness Analysis of Model Predictive Controller (MPC) and Proportional Integral Derivative Control (PID) of an Autonomous Vehicle for Lane Keeping Maneuvers

2019 ◽  
Author(s):  
Moveh Samuel
Keyword(s):  
Autonomous Vehicle ◽  
Robustness Analysis ◽  
Driving Safety ◽  
Reference Trajectory ◽  
Proportional Integral Derivative ◽  
Cruise Control ◽  
Proportional Integral ◽  
Proportional Integral Derivative Control ◽  
Lane Keeping ◽  
Derivative Control

Motivated by Autonomous vehicle idea and driving safety issues, driver assistance system such as Active braking, Cruise Control, Lane departure warning lane keeping and etc. has become a very active research area. However, this paper presents the performance and robustness analysis of a model predictive control and proportional integral derivative control for lane keeping maneuvers of an autonomous vehicle using computer vision simulation studies. A simulation study was carried out where a vehicle model based on single tracked bicycle model was developed in MATLAB/SIMULINK environment together with a vision dynamic system. Both PID controller and MPC were simulated to maintain the desired reference trajectory of the vehicle by controlling steering angle. Further performance and robustness analysis were carried out and the simulation results show that the proposed control system for the PID control achieved its objective even though it was less robust in maintaining its performance under various conditions like vehicle load change, different longitudinal speed and different cornering stiffness. While in the case of MPC the optimizer made sure that the predicted future trajectory of the vehicle output tracks the desired reference trajectory and was more robust in maintaining its performance under same conditions as in PID.

scholarly journals Transporting Children in Autonomous Vehicles: An Exploratory Study

2019 ◽  
Vol 62 (2) ◽  
pp. 278-287 ◽  
Author(s):  
Patrice D. Tremoulet ◽  
Thomas Seacrist ◽  
Chelsea Ward McIntosh ◽  
Helen Loeb ◽  
Anna DiPietro ◽  
...  
Keyword(s):  
Extracurricular Activities ◽  
Autonomous Vehicles ◽  
Driving Simulator ◽  
Autonomous Vehicle ◽  
Remote Access ◽  
Unaccompanied Children ◽  
Potential Applications ◽  
Minimum Age ◽  
Audio Communication ◽  
Vehicle Information

Objective Identify factors that impact parents’ decisions about allowing an unaccompanied child to ride in an autonomous vehicle (AV). Background AVs are being tested in several U.S. cities and on highways in multiple states. Meanwhile, suburban parents are using ridesharing services to shuttle children from school to extracurricular activities. Parents may soon be able to hire AVs to transport children. Method Nineteen parents of 8- to 16-year-old children, and some of their children, rode in a driving simulator in autonomous mode, then were interviewed. Parents also participated in focus groups. Topics included minimum age for solo child passengers, types of trips unaccompanied children might take, and vehicle features needed to support child passengers. Results Parents would require two-way audio communication and prefer video feeds of vehicle interiors, seatbelt checks, automatic locking, secure passenger identification, and remote access to vehicle information. Parents cited convenience as the greatest benefit and fear that AVs could not protect passengers during unplanned trip interruptions as their greatest concern. Conclusion Manufacturers have an opportunity to design family-friendly AVs from the outset, rather than retrofit them to be safe for child passengers. More research, especially usability studies where families interact with technology prototypes, is needed to understand how AV design impacts child passengers. Application Potential applications of this research include not only designing vehicles that can be used to safely transport children, seniors who no longer drive, and individuals with disabilities but also developing regulations, policies, and societal infrastructure to support safe child transport via AVs.

Proportional Navigation and Model Predictive Control of an Unmanned Autonomous Vehicle for Obstacle Avoidance

2018 ◽  
Author(s):  
Ryan P. Shaw ◽  
David M. Bevly
Keyword(s):  
Obstacle Avoidance ◽  
Autonomous Vehicle ◽  
Integrated System ◽  
Reference Trajectory ◽  
Ground Vehicles ◽  
Proportional Navigation ◽  
Predictive Controller ◽  
Proportional Navigation Law ◽  
Vehicle Dynamic Model ◽  
And Control

This paper presents a new approach for the guidance and control of a UGV (Unmanned Ground Vehicle). An obstacle avoidance algorithm was developed using an integrated system involving proportional navigation (PN) and a nonlinear model predictive controller (NMPC). An obstacle avoidance variant of the classical proportional navigation law generates command lateral accelerations to avoid obstacles, while the NMPC is used to track the reference trajectory given by the PN. The NMPC utilizes a lateral vehicle dynamic model. Obstacle avoidance has become a popular area of research for both unmanned aerial vehicles and unmanned ground vehicles. In this application an obstacle avoidance algorithm can take over the control of a vehicle until the obstacle is no longer a threat. The performance of the obstacle avoidance algorithm is evaluated through simulation. Simulation results show a promising approach to conditionally implemented obstacle avoidance.

Robust Approximate Constraint-Following Control for Autonomous Vehicle Platoon Systems

2017 ◽  
Vol 20 (4) ◽  
pp. 1611-1623 ◽  
Author(s):  
Xiaomin Zhao ◽  
Y. H. Chen ◽  
Han Zhao
Keyword(s):  
Autonomous Vehicle ◽  
Vehicle Platoon

Development of a Driving Simulator for Virtual Evaluation of Vehicle Dynamics

2010 ◽  
Author(s):  
A. M. Sharaf
Keyword(s):  
Performance Evaluation ◽  
Vehicle Dynamics ◽  
Driving Simulator ◽  
Field Testing ◽  
Vehicle Performance ◽  
Vehicle Simulation ◽  
Virtual Platform ◽  
The Cost ◽  
Vehicle Testing ◽  
Simulation Package

This paper delineates the conceptual algorithms of a driving simulator which is intended for vehicle performance evaluation and to act as a virtual platform for research studies and therefore eliminates the cost and dangerous of field testing. A virtual proving ground for vehicle testing has been devised through which virtual handling, traction and ride tests can be performed. A fully instrumented simulator cabin combining the driver and the vehicle simulation package is developed. Different vehicle configurations are simulated during typical sever manoeuvres which reflects the robustness and fidelity of the devised simulator.

scholarly journals Risk Assessment by a Passenger of an Autonomous Vehicle Among Pedestrians: Relationship Between Subjective and Physiological Measures

2021 ◽  
Vol 2 ◽  
Author(s):  
Jeffery Petit ◽  
Camilo Charron ◽  
Franck Mars
Keyword(s):  
Risk Perception ◽  
Autonomous Navigation ◽  
Driving Simulator ◽  
Electrodermal Activity ◽  
Autonomous Vehicle ◽  
Information Criterion ◽  
Redundant Data ◽  
Safety Margins ◽  
Subjective Risk ◽  
Two Systems

Autonomous navigation becomes complex when it is performed in an environment that lacks road signs and includes a variety of users, including vulnerable pedestrians. This article deals with the perception of collision risk from the viewpoint of a passenger sitting in the driver's seat who has delegated the total control of their vehicle to an autonomous system. The proposed study is based on an experiment that used a fixed-base driving simulator. The study was conducted using a group of 20 volunteer participants. Scenarios were developed to simulate avoidance manoeuvres that involved pedestrians walking at 4.5 kph and an autonomous vehicle that was otherwise driving in a straight line at 30 kph. The main objective was to compare two systems of risk perception: These included subjective risk assessments obtained with an analogue handset provided to the participants and electrodermal activity (EDA) that was measured using skin conductance sensors. The relationship between these two types of measures, which possibly relates to the two systems of risk perception, is not unequivocally described in the literature. This experiment addresses this relationship by manipulating two factors: The time-to-collision (TTC) at the initiation of a pedestrian avoidance manoeuvre and the lateral offset left between a vehicle and a pedestrian. These manipulations of vehicle dynamics made it possible to simulate different safety margins regarding pedestrians during avoidance manoeuvres. The conditional dependencies between the two systems and the manipulated factors were studied using hybrid Bayesian networks. This relationship was inferred by selecting the best Bayesian network structure based on the Bayesian information criterion. The results demonstrate that the reduction of safety margins increases risk perception according to both types of indicators. However, the increase in subjective risk is more pronounced than the physiological response. While the indicators cannot be considered redundant, data modeling suggests that the two risk perception systems are not independent.

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.

Unfalsified control design using a generalized cost function for a quadrotor

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Azam Hokmabadi ◽  
Mahdi Khodabandeh
Keyword(s):  
Adaptive Control ◽  
Cost Function ◽  
Sliding Mode ◽  
Control Design ◽  
Robust Adaptive Control ◽  
Operating Conditions ◽  
System Model ◽  
Reference Trajectory ◽  
Content Type ◽  
Generalized Cost

Purpose The purpose of this paper is to design a controller for a quadrotor only by using input–output data without a need for the system model. Design/methodology/approach Tracking control for the quadrotor is considered by using unfalsified control, which is one of the most recent strategies of robust adaptive control. The main assumption in unfalsified control design is that there is no access to the system model. Also, ideal path tracking and controlling the quadrotor are been paid attention to in the presence of external disturbances and uncertainties. First, unfalsified control method is introduced which is a data-driven and model-free approach in the field of adaptive control. Next, model of the quadrotor and unfalsified control design for the quadrotor are presented. Second, design of a control bank consisting of four proportional integral derivative controllers and a sliding mode controller is carried out. Findings A particular innovation on an unfalsified control algorithm in this paper is use of a generalized cost function in the hysteresis switching algorithm to find the best controller. Originality/value Finally, the performance and robustness of the designed controllers are investigated by simulation studies in various operating conditions including reference trajectory changes, facing to wind disturbance, uncertainty of the system and changes in payload, which show acceptable performances.

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