scholarly journals Risk-Sensitive Rear-Wheel Steering Control Method Based on the Risk Potential Field

2021 ◽  
Vol 11 (16) ◽  
pp. 7296
Author(s):  
Toshinori Kojima ◽  
Pongsathorn Raksincharoensak
Keyword(s):  
Potential Field ◽  
Autonomous Driving ◽  
Rear Wheel ◽  
Control Law ◽  
Steering Control ◽  
Control Command ◽  
Driving Assistance ◽  
Assistance Systems ◽  
Lane Change Test ◽  
Risk Potential

Various driving assistance systems have been developed to reduce the number of automobile accidents. However, the control laws of these assistance systems differ based on each situation, and the discontinuous control command value may be input instantaneously. Therefore, a seamless and unified control law for driving assistance systems that can be used in multiple situations is necessary to realize more versatile autonomous driving. Although studies have been conducted on four-wheel steering that steers the rear wheels, these studies considered the role of the rear wheels only to improve vehicle dynamics and not to contribute to autonomous driving. Therefore, in this study, we define the risk potential field as a uniform control law and propose a rear-wheel steering control system that actively steers the rear wheels to contribute to autonomous driving, depending on the level of the perceived risk in the driving situation. The effectiveness of the proposed method is verified by a double lane change test, which is performed assuming emergency avoidance in simulations, and subject experiments using a driving simulator. The results indicate that actively steering the rear wheels ensures a safer and smoother drive while simultaneously improving the emergency avoidance performance.

Towards of a modular framework for semi-autonomous driving assistance systems

2018 ◽  
Vol 13 (1) ◽  
pp. 111-120 ◽  
Author(s):  
Luis A. Curiel-Ramirez ◽  
Ricardo A. Ramirez-Mendoza ◽  
Gerardo Carrera ◽  
Javier Izquierdo-Reyes ◽  
M. Rogelio Bustamante-Bello
Keyword(s):  
Autonomous Driving ◽  
Driving Assistance ◽  
Assistance Systems ◽  
Modular Framework

scholarly journals Digital Maps for Driving Assistance Systems and Autonomous Driving

2016 ◽  
pp. 201-244 ◽  
Author(s):  
Alexandre Armand ◽  
Javier Ibanez-Guzman ◽  
Clément Zinoune
Keyword(s):  
Autonomous Driving ◽  
Digital Maps ◽  
Driving Assistance ◽  
Assistance Systems

Real-Time Detection of Road Lane-Lines for Autonomous Driving

2020 ◽  
Vol 13 (2) ◽  
pp. 265-274 ◽  
Author(s):  
Wael Farag
Keyword(s):  
Autonomous Driving ◽  
Line Segments ◽  
The Road ◽  
Detection And Tracking ◽  
Main Emphasis ◽  
Driving Assistance ◽  
Lane Line ◽  
Rgb Images ◽  
Assistance Systems ◽  
Self Driving Cars

Background: Enabling fast and reliable lane-lines detection and tracking for advanced driving assistance systems and self-driving cars. Methods: The proposed technique is mainly a pipeline of computer vision algorithms that augment each other and take in raw RGB images to produce the required lane-line segments that represent the boundary of the road for the car. The main emphasis of the proposed technique in on simplicity and fast computation capability so that it can be embedded in affordable CPUs that are employed by ADAS systems. Results: Each used algorithm is described in details, implemented and its performance is evaluated using actual road images and videos captured by the front mounted camera of the car. The whole pipeline performance is also tested and evaluated on real videos. Conclusion: The evaluation of the proposed technique shows that it reliably detects and tracks road boundaries under various conditions.

Independent wheel control system design for highly automated driving

2021 ◽  
pp. 095440702110065
Author(s):  
Shihuan Li ◽  
Lei Wang
Keyword(s):  
Control System ◽  
Control Method ◽  
Autonomous Driving ◽  
Control System Design ◽  
Steering Control ◽  
Automated Driving ◽  
Accuracy And Precision ◽  
Actual Observation ◽  
Control Scheme ◽  
Interference Measurement

For L4 and above autonomous driving levels, the automatic control system has been redundantly designed, and a new steering control method based on brake has been proposed; a new dual-track model has been established through multiple driving tests. The axle part of the model was improved, the accuracy of the transfer function of the model was verified again through acceleration-slide tests; a controller based on interference measurement was designed on the basis of the model, and the relationships between the controller parameters was discussed. Through the linearization of the controller, the robustness of uncertain automobile parameters is discussed; the control scheme is tested and verified through group driving test, and the results prove that the accuracy and precision of the controller meet the requirements, the robustness stability is good. Moreover, the predicted value of the model fits well with the actual observation value, the proposal of this method provides a new idea for avoiding car out of control.

Three-Dimensional Curve Tracking for Particles Using Gyroscopic Control

2017 ◽  
Vol 139 (12) ◽  
Author(s):  
Chuanfeng Wang
Keyword(s):  
Tracking Control ◽  
Three Dimensional ◽  
Smooth Curve ◽  
Autonomous Agent ◽  
Control Law ◽  
Steering Control ◽  
3D Space ◽  
Moving Direction ◽  
Tangent Direction ◽  
Curve Tracking

Curve-tracking control is challenging and fundamental in many robotic applications for an autonomous agent to follow a desired path. In this paper, we consider a particle, representing a fully actuated autonomous robot, moving at unit speed under steering control in the three-dimensional (3D) space. We develop a feedback control law that enables the particle to track any smooth curve in the 3D space. Representing the 3D curve in the natural Frenet frame, we construct the control law under which the moving direction of the particle will be aligned with the tangent direction of the desired curve and the distance between the particle and the desired curve will converge to zero. We demonstrate the effectiveness of the proposed 3D curve-tracking control law in simulations.

Towards Automated Bicycles: Achieving Self-Balance Using Steering Control

2018 ◽  
Author(s):  
Wenhao Deng ◽  
Skyler Moore ◽  
Jonathan Bush ◽  
Miles Mabey ◽  
Wenlong Zhang
Keyword(s):  
Autonomous Vehicles ◽  
Controller Design ◽  
Smart Cities ◽  
Autonomous Driving ◽  
Human Robot Interaction ◽  
Steering Control ◽  
Control Moment Gyroscope ◽  
Control Moment ◽  
Potential Applications ◽  
Electric Bicycle

In recent years, researchers from both academia and industry have worked on connected and automated vehicles and they have made great progress toward bringing them into reality. Compared to automated cars, bicycles are more affordable to daily commuters, as well as more environmentally friendly. When comparing the risk posed by autonomous vehicles to pedestrians and motorists, automated bicycles are much safer than autonomous cars, which also allows potential applications in smart cities, rehabilitation, and exercise. The biggest challenge in automating bicycles is the inherent problem of staying balanced. This paper presents a modified electric bicycle to allow real-time monitoring of the roll angles and motor-assisted steering. Stable and robust steering controllers for bicycle are designed and implemented to achieve self-balance at different forward speeds. Tests at different speeds have been conducted to verify the effectiveness of hardware development and controller design. The preliminary design using a control moment gyroscope (CMG) to achieve self-balancing at lower speeds are also presented in this work. This work can serve as a solid foundation for future study of human-robot interaction and autonomous driving.

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