scholarly journals Digital Maps for Driving Assistance Systems and Autonomous Driving

2016 ◽  
pp. 201-244 ◽  
Author(s):  
Alexandre Armand ◽  
Javier Ibanez-Guzman ◽  
Clément Zinoune
Author(s):  
Luis A. Curiel-Ramirez ◽  
Ricardo A. Ramirez-Mendoza ◽  
Gerardo Carrera ◽  
Javier Izquierdo-Reyes ◽  
M. Rogelio Bustamante-Bello

2021 ◽  
Vol 11 (16) ◽  
pp. 7296
Author(s):  
Toshinori Kojima ◽  
Pongsathorn Raksincharoensak

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.


2020 ◽  
Vol 13 (2) ◽  
pp. 265-274 ◽  
Author(s):  
Wael Farag

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.


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