scholarly journals CHARACTERIZING DRIVER TAKE-OVER ACCURACY: EFFECT OF AGE, SEX, STARTLE, AND SECONDARY TASK

2021 ◽  
Vol 57 (2) ◽  
pp. 281-289
Author(s):  
Valentina Graci ◽  
◽  
Meta Austin ◽  
Madeline Griffith ◽  
Rahul Akkem ◽  
...  
Keyword(s):  
Secondary Task ◽  
Autonomous Vehicle ◽  
Steering Wheel ◽  
Alignment Error ◽  
Steering Control ◽  
Male Adult ◽  
Correct Alignment ◽  
Wheel Alignment ◽  
Alignment Angle ◽  
Study Participants

The Acoustic Startling Pre-stimulus (ASPS, i.e. a loud sound preceding a physical perturbation was previously found to accelerate take-over actions in adults but not teens in autonomous vehicle scenarios. It is not clear if the ASPS also influences the accuracy of the take-over response across ages and sexes. Therefore the aims of this study are: to characterize take-over accuracy across age/experience and sex and to examine the effect of the ASPS and a secondary task on steering wheel alignment in autonomous vehicle take-over scenarios. Fourteen adult (7 males and 13 teenage (6 males drivers volunteered for this study. Participants were instructed to align a marker on the steering wheel with a marker on a lateral post as fast as they could, when a sled perturbation started. Two of the conditions included the ASPS. Two of the conditions involved mobile texting while the sled started moving. The angle between the steering wheel and the lateral post was used to quantify overshooting, undershooting, or correct alignment during steering. Results showed that adult female subjects reached correct alignment slightly more frequently than any other group, while male adult drivers decreased their alignment error after the first trial. Both female and male adult drivers had a reduced alignment angle when the first trial had an ASPS compared to when the first trial had no ASPS while teen drivers performed similarly with ASPS or without. This study showed that take-over accuracy and steering control are influenced by sex, age/experience, and a startle-based warning.

scholarly journals Establishment and tracking control of trapezoidal steering wheel angle model for autonomous vehicles

2020 ◽  
Vol 17 (6) ◽  
pp. 172988142098278
Author(s):  
Haobin Jiang ◽  
Aoxue Li ◽  
Xinchen Zhou ◽  
Yue Yu
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Steering System ◽  
Free Play ◽  
Driver Model ◽  
Steering Wheel ◽  
Feature Points ◽  
Steering Control ◽  
Power Steering ◽  
Nonlinear Vehicle Model

Human drivers have rich and diverse driving characteristics on curved roads. Finding the characteristic quantities of the experienced drivers during curve driving and applying them to the steering control of autonomous vehicles is the research goal of this article. We first recruited 10 taxi drivers, 5 bus drivers, and 5 driving instructors as the representatives of experienced drivers and conducted a real car field experiment on six curves with different lengths and curvatures. After processing the collected driving data in the Frenet frame and considering the free play of a real car’s steering system, it was interesting to observe that the shape enclosed by steering wheel angles and the coordinate axis was a trapezoid. Then, we defined four feature points, four feature distances, and one feature steering wheel angle, and the trapezoidal steering wheel angle (TSWA) model was developed by backpropagation neural network with the inputs were vehicle speeds at four feature points, and road curvature and the outputs were feature distances and feature steering wheel angle. The comparisons between TSWA model and experienced drivers, model predictive control, and preview-based driver model showed that the proposed TSWA model can best reflect the steering features of experienced drivers. What is more, the concise expression and human-like characteristic of TSWA model make it easy to realize human-like steering control for autonomous vehicles. Lastly, an autonomous vehicle composed of a nonlinear vehicle model and electric power steering (EPS) system was established in Simulink, the steering wheel angles generated by TSWA model were tracked by EPS motor directly, and the results showed that the EPS system can track the steering angles with high accuracy at different vehicle speeds.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

Driver Steering Control and a New Perspective on Car Handling Qualities

2005 ◽  
Vol 219 (10) ◽  
pp. 1041-1051 ◽  
Author(s):  
R S Sharp
Keyword(s):  
White Noise ◽  
Gaussian White Noise ◽  
Design Parameters ◽  
Steering Wheel ◽  
Steering Control ◽  
Pass Filter ◽  
Low Pass Filter ◽  
Handling Qualities ◽  
Gaussian White Noise Process ◽  
Low Pass

The article is about steering control of cars by drivers, concentrating on following the lateral profile of the roadway, which is presumed visible ahead of the car. It builds on previously published work, in which it was shown how the driver's preview of the roadway can be combined with the linear dynamics of a simple car to yield a problem of discrete-time optimal-linear-control-theory form. In that work, it was shown how an optimal ‘driver’ of a linear car can convert the path preview sample values, modelled as deriving from a Gaussian white-noise process, into steering wheel displacement commands to cause the car to follow the previewed path with an attractive compromise between precision and ease. Recognizing that real roadway excitation is not so rich in high frequencies as white-noise, a low-pass filter is added to the system. The white-noise sample values are filtered before being seen by the driver. Numerical results are used to show that the optimal preview control is unaltered by the inclusion of the low-pass filter, whereas the feedback control is affected diminishingly as the preview increases. Then, using the established theoretical basis, new results are generated to show time-invariant optimal preview controls for cars and drivers with different layouts and priorities. Tight and loose controls, representing different balances between tracking accuracy and control effort, are calculated and illustrated through simulation. A new performance criterion with handling qualities implications is set up, involving the minimization of the preview distance required. The sensitivities of this distance to variations in the car design parameters are calculated. The influence of additional rear wheel steering is studied from the viewpoint of the preview distance required and the form of the optimal preview gain sequence. Path-following simulations are used to illustrate relatively high-authority and relatively low-authority control strategies, showing manoeuvring well in advance of a turn under appropriate circumstances. The results yield new insights into driver steering control behaviour and vehicle design optimization. The article concludes with a discussion of research in progress aimed at a further improved understanding of how drivers control their vehicles.

Effectiveness of the running system of a wheeled vehicle

2020 ◽  
pp. 16-22
Author(s):  
D.A. Dubovik
Keyword(s):  
Steering Wheel ◽  
Steering Control ◽  
Control Effectiveness ◽  
Power Drive ◽  
Running System ◽  
Wheeled Vehicle ◽  
Curvilinear Motion ◽  
Wheeled Vehicles ◽  
System Power ◽  
Effectiveness Coefficient

A method for quantitative assessment of the effectiveness of the running system of wheeled vehicles for the general case of curvilinear motion is proposed. An expression is obtained for calculating the coefficient of efficiency of the running system of a wheeled vehicle, taking into account the parameters of the power and steering wheel drives. The results of evaluating the effectiveness of the running system of an off-road vehicle with a wheel arrangement of 8Ѕ8 and two front steerable axles are presented. Keywords: wheeled vehicle, running system, power drive, drive wheels, steering control, effectiveness, coefficient of efficiency. [email protected]

Co-Simulation of Power Steering Control and Vehicle Dynamic Responses

2001 ◽  
Author(s):  
Gene Y. Liao
Keyword(s):  
Dynamic Model ◽  
General Purpose ◽  
Dynamic Responses ◽  
Steering Wheel ◽  
Vehicle Dynamic ◽  
Steering Control ◽  
Integrated Simulation ◽  
Full Vehicle ◽  
Power Steering ◽  
Vehicle Dynamic Model

Abstract Many general-purpose and specialized simulation codes are becoming more flexible which allows analyses to be carried out simultaneously in a coupled manner called co-simulation. Using co-simulation technique, this paper develops an integrated simulation of an Electric Power Steering (EPS) control system with a full vehicle dynamic model. A full vehicle dynamic model interacting with EPS control algorithm is concurrently simulated on a single bump road condition. The effects of EPS on the vehicle dynamic behavior and handling responses resulting from steer and road input are analyzed and compared with proving ground experimental data. The comparisons show reasonable agreement on tie-rod load, rack displacement, steering wheel torque and tire center acceleration. This developed co-simulation capability may be useful for EPS performance evaluation and calibration as well as for vehicle handling performance integration.

Steering Control Based on the Yaw Rate and Projected Steering Wheel Angle in Evasion Maneuvers

2018 ◽  
Author(s):  
Yifan Ye ◽  
Jian Zhao ◽  
Jian Wu ◽  
Bing Zhu ◽  
Yang Zhao ◽  
...  
Keyword(s):  
Steering Wheel ◽  
Steering Control ◽  
Yaw Rate

scholarly journals Using Naturalistic Vehicle-Based Data to Predict Distraction and Environmental Demand

2019 ◽  
Vol 11 (3) ◽  
pp. 59-70
Author(s):  
Dina Kanaan ◽  
Suzan Ayas ◽  
Birsen Donmez ◽  
Martina Risteska ◽  
Joyita Chakraborty
Keyword(s):  
Motor Control ◽  
Secondary Task ◽  
Markov Models ◽  
Task Engagement ◽  
Steering Wheel ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Environmental Demand ◽  
Driving Task ◽  
Behavior Models

This research utilized vehicle-based measures from a naturalistic driving dataset to detect distraction as indicated by long off-path glances (≥ 2 s) and whether the driver was engaged in a secondary (non-driving) task or not, as well as to estimate motor control difficulty associated with the driving environment (i.e. curvature and poor surface conditions). Advanced driver assistance systems can exploit such driver behavior models to better support the driver and improve safety. Given the temporal nature of vehicle-based measures, Hidden Markov Models (HMMs) were utilized; GPS speed and steering wheel position were used to classify the existence of off-path glances (yes vs. no) and secondary task engagement (yes vs. no); lateral (x-axis) and longitudinal (y-axis) acceleration were used to classify motor control difficulty (lower vs. higher). Best classification accuracies were achieved for identifying cases of long off-path glances and secondary task engagement with both accuracies of 77%.

Active steering control for autonomous vehicles based on a driver-in-the-loop platform: A case study of collision avoidance

2019 ◽  
Vol 233 (10) ◽  
pp. 1422-1437
Author(s):  
Keji Chen ◽  
Xiaofei Pei ◽  
Daoyuan Sun ◽  
Zhenfu Chen ◽  
Xuexun Guo ◽  
...  
Keyword(s):  
Risk Assessment ◽  
Model Predictive Control ◽  
Collision Avoidance ◽  
Predictive Control ◽  
Autonomous Vehicle ◽  
Steering Control ◽  
Safe Driving ◽  
Active Steering ◽  
Active Steering Control

Leveraging the advancements in sensor and mapping technologies, the collision-free autonomous vehicle becomes possible in the future. In this article, a case study of collision avoidance by active steering control is presented and verified by a driver-in-the-loop platform. The proposed control system integrates a risk assessment algorithm and a hierarchical model predictive control approach to ensure a safe driving. First, a fuzzy logic is used to estimate the potential conflict. Besides, a nonlinear model predictive control is introduced in the upper layer of the model predictive controller to generate a collision-free trajectory. Furthermore, the lower layer determines the optimal steering angle based on the linear time-variant model predictive control to follow the replanning path. The performance of the controller has been evaluated in the real-time driver-in-the-loop test. The results show that the autonomous vehicle is able to avoid the collision with the surrounding vehicle that is operated by a real driver, and the performance of collision avoidance is improved by means of the risk assessment.

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