scholarly journals Human–Machine Cooperative Control of Intelligent Vehicles for Lane Keeping—Considering Safety of the Intended Functionality

Actuators ◽  
2021 ◽  
Vol 10 (9) ◽  
pp. 210
Author(s):  
Mingyue Yan ◽  
Wuwei Chen ◽  
Qidong Wang ◽  
Linfeng Zhao ◽  
Xiutian Liang ◽  
...  
Keyword(s):  
Cooperative Control ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Intelligent Vehicles ◽  
Driver Model ◽  
Lane Departure ◽  
Extension Model ◽  
Shared Authority ◽  
Driver Error ◽  
Lane Keeping

Reasonably foreseeable misuse by persons, as a primary aspect of safety of the intended functionality (SOTIF), has a significant effect on cooperation performance for lane keeping. This paper presents a novel human–machine cooperative control scheme with consideration of SOTIF issues caused by driver error. It is challenging to balance lane keeping performance and driving freedom when driver error occurs. A safety evaluation strategy is proposed for safety supervision, containing assessments of driver error and lane departure risk caused by driver error. A dynamic evaluation model of driver error is designed based on a typical driver model in the loop to deal with the uncertainty and variability of driver behavior. Additionally, an extension model is established for determining the cooperation domain. Then, an authority allocation strategy is proposed to generate a dynamic shared authority and achieve an adequate balance between lane keeping performance and driving freedom. Finally, a model predictive control (MPC)-based controller is designed for calculating optimal steering angle, and a steer-by-wheel (SBW) system is employed as an actuator. Numerical simulation tests are conducted on driver error scenarios based on the CarSim and MATLAB/Simulink software platforms. The simulation results demonstrate the effectiveness of the proposed method.

Study on Highway Safety Simulation Evaluation Model and Method

2012 ◽  
Vol 170-173 ◽  
pp. 3677-3682
Author(s):  
Yong Qiu Wei ◽  
Li Ping Li ◽  
Hong Zhi Yang
Keyword(s):  
Computer Simulation ◽  
Simulation Model ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Simulation Models ◽  
Simulation System ◽  
Highway Safety ◽  
Driver Model ◽  
Highway Design ◽  
Vehicle Operation

We analyzed the characteristics of highway simulation system; established framework for a Multi-Agent based simulation system, and identified the construction framework for the highway safety simulation model. Through constructing computer simulation models – vehicle model, driver model, road model and barrier crash simulation model – to simulate vehicle operation on highway, we obtained quantitative indices such as operating speed, acceleration, and brake requirement, and evaluated highway design specifications. In consideration of specific highway project, we analyzed and evaluated the application effect of the simulation model in alignment safety evaluation and barrier setting safety evaluation, confirmed that the practicality of the simulation model. The result showed that computer simulation can help designers find design problems during the design period, make timely correction, and improve the scientificity and appropriateness.

Vehicle assistance for lane keeping, lane departure avoidance and yaw stability. Approach by simultaneous steering and differential braking

2010 ◽  
Vol 44 (7) ◽  
pp. 811-851
Author(s):  
Nicoleta Minoiu enache ◽  
Saïd Mammar ◽  
Sébastien Glaser ◽  
Benoit Lusetti
Keyword(s):  
Lane Departure ◽  
Yaw Stability ◽  
Lane Keeping

Modeling human-like longitudinal driver model for intelligent vehicles based on reinforcement learning

2021 ◽  
pp. 095440702098357
Author(s):  
Ju Xie ◽  
Xing Xu ◽  
Feng Wang ◽  
Haobin Jiang
Keyword(s):  
Reinforcement Learning ◽  
Comprehensive Evaluation ◽  
Path Following ◽  
Intelligent Vehicles ◽  
Driver Model ◽  
Control Center ◽  
Training Performance ◽  
Learning Agents ◽  
System A ◽  
And Control

The driver model is the decision-making and control center of intelligent vehicle. In order to improve the adaptability of intelligent vehicles under complex driving conditions, and simulate the manipulation characteristics of the skilled driver under the driver-vehicle-road closed-loop system, a kind of human-like longitudinal driver model for intelligent vehicles based on reinforcement learning is proposed. This paper builds the lateral driver model for intelligent vehicles based on optimal preview control theory. Then, the control correction link of longitudinal driver model is established to calculate the throttle opening or brake pedal travel for the desired longitudinal acceleration. Moreover, the reinforcement learning agents for longitudinal driver model is parallel trained by comprehensive evaluation index and skilled driver data. Lastly, training performance and scenarios verification between the simulation experiment and the real car test are performed to verify the effectiveness of the reinforcement learning based longitudinal driver model. The results show that the proposed human-like longitudinal driver model based on reinforcement learning can help intelligent vehicles effectively imitate the speed control behavior of the skilled driver in various path-following scenarios.

Town Traffic Safety Evaluation Model Based on PSR – SPA

ICCTP 2011 ◽  
2011 ◽  
Author(s):  
Jianxiao Ma ◽  
Taowei Yan ◽  
Li Lin ◽  
Jingwen Jiang
Keyword(s):  
Traffic Safety ◽  
Evaluation Model ◽  
Safety Evaluation ◽  
Model Based

scholarly journals Social Force Model-Based Safety Evaluation of Intersections in Arterials Considering the Pedestrian Yield Rule

2021 ◽  
Vol 18 (23) ◽  
pp. 12461
Author(s):  
Jiao Yao ◽  
Yuhang Li ◽  
Jiaping He
Keyword(s):  
Evaluation Model ◽  
Safety Evaluation ◽  
Force Model ◽  
Social Force ◽  
Trajectory Data ◽  
Entropy Weight ◽  
Social Force Model ◽  
Entropy Weight Method ◽  
Safety Models ◽  
Pedestrian Crossing

To enhance the safety of pedestrians crossing the street, a series of new regulations regarding pedestrian yield has been proposed and widely implemented across cities. In this study, we first made some improvements to the social force model, in which pedestrian crossing at the intersection, drivers’ psychology of giving way, vehicle yield to pedestrians, vehicle yield in different directions, the influence of pedestrians crossing boundaries, and signal lamp groups on pedestrian behavior were considered. Furthermore, pedestrian crossing and vehicle yield safety models were established, based on which the comprehensive safety evaluation model of intersections in arterials was established, in which two indices—(1) the safety degree of pedestrian crossings and (2) vehicle acceleration interference—were combined with the entropy weight method. Finally, four types of intersections in arterials were studied using a simulation: the intersections between different levels of arterials, and intersections with one-time and two-times pedestrian crossings. Moreover, safety evaluation and analysis of those intersections, considering the rule of pedestrian yield, were conducted combined with the trajectory data from the VISSIM simulation. The relevant results showed that for pedestrians crossing the street, the pedestrian safety of two-time crossing is significantly higher than that of one-time crossing, and compared with the arterial, the pedestrian crossing distance of the sub-arterial is shorter, and the pedestrian perception is safer. Moreover, due to the herd psychology effect, the increase in pedestrian flow volume improves the safety perception of pedestrians at the intersection.

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