scholarly journals Towards Reliable Multisensory Perception and Its Automotive Applications

2020 ◽  
Vol 48 (4) ◽  
pp. 334-340 ◽  
Author(s):  
András Rövid ◽  
Viktor Remeli ◽  
Norbert Paufler ◽  
Henrietta Lengyel ◽  
Máté Zöldy ◽  
...  
Keyword(s):  
Object Detection ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Lane Detection ◽  
Multisensory Perception ◽  
Automotive Applications ◽  
Environment Modeling ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection

Autonomous driving poses numerous challenging problems, one of which is perceiving and understanding the environment. Since self-driving is safety critical and many actions taken during driving rely on the outcome of various perception algorithms (for instance all traffic participants and infrastructural objects in the vehicle's surroundings must reliably be recognized and localized), thus the perception might be considered as one of the most critical subsystems in an autonomous vehicle. Although the perception itself might further be decomposed into various sub-problems, such as object detection, lane detection, traffic sign detection, environment modeling, etc. In this paper the focus is on fusion models in general (giving support for multisensory data processing) and some related automotive applications such as object detection, traffic sign recognition, end-to-end driving models and an example of taking decisions in multi-criterial traffic situations that are complex for both human drivers and for the self-driving vehicles as well.

Autonomous Vehicle Application for Improving Traffic Sign Learning near Ramps

2020 ◽  
Vol 2674 (10) ◽  
pp. 158-166
Author(s):  
Zhenhua Zhang ◽  
Leon Stenneth ◽  
Xiyuan Liu
Keyword(s):  
State Of The Art ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Speed Reduction ◽  
Sign Recognition ◽  
Path Information ◽  
Real World Applications ◽  
Location Adjustment

The state-of-the-art traffic sign recognition (TSR) algorithms are designed to recognize the textual information of a traffic sign at over 95% accuracy. Even though, they are still not ready for complex roadworks near ramps. In real-world applications, when the vehicles are running on the freeway, they may misdetect the traffic signs for the ramp, which will become inaccurate feedback to the autonomous driving applications and result in unexpected speed reduction. The misdetection problems have drawn minimal attention in recent TSR studies. In this paper, it is proposed that the existing TSR studies should transform from the point-based sign recognition to path-based sign learning. In the proposed pipeline, the confidence of the TSR observations from normal vehicles can be increased by clustering and location adjustment. A supervised learning model is employed to classify the clustered learned signs and complement their path information. Test drives are conducted in 12 European countries to calibrate the models and validate the path information of the learned sign. After model implementation, the path accuracy over 1,000 learned signs can be increased from 75.04% to 89.80%. This study proves the necessity of the path-based TSR studies near freeway ramps and the proposed pipeline demonstrates a good utility and broad applicability for sensor-based autonomous vehicle applications.

An Approach of Traffic Sign Recognition Algorithm on MATLAB

2014 ◽  
Vol 644-650 ◽  
pp. 3980-3983
Author(s):  
Jia Yang Li ◽  
Mei Xia Song
Keyword(s):  
Real Time ◽  
Intelligent Transportation System ◽  
Recognition System ◽  
Recognition Algorithm ◽  
Traffic Sign Recognition ◽  
Traffic Sign ◽  
Sign Recognition ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign recognition system is a great important part of intelligent transportation system and advanced auxiliary driving system, and it is a key problem to improve the accuracy and real-time performance of traffic sign detection in reality.Considering to the perspective of accuracy and real-time of traffic sign detection and recognition, this article built the traffic sign detection and recognition method based on MATLAB. Finally, the paper proved the conclusion, and future traffic sign detection and recognition need to be further research topics and practical application prospect.

scholarly journals TRAFFIC SIGN INVENTORY FROM GOOGLE STREET VIEW IMAGES

2016 ◽  
Vol XLI-B4 ◽  
pp. 243-246
Author(s):  
Victor J. D. Tsai ◽  
Jyun-Han Chen ◽  
Hsun-Sheng Huang
Keyword(s):  
Intelligent Transportation Systems ◽  
Low Cost ◽  
Autonomous Vehicle ◽  
Transportation Systems ◽  
Traffic Sign ◽  
Google Street View ◽  
Street View ◽  
Sign Detection ◽  
Traffic Sign Detection ◽  
Detection And Recognition

Traffic sign detection and recognition (TSDR) has drawn considerable attention on developing intelligent transportation systems (ITS) and autonomous vehicle driving systems (AVDS) since 1980’s. Unlikely to the general TSDR systems that deal with real-time images captured by the in-vehicle cameras, this research aims on developing techniques for detecting, extracting, and positioning of traffic signs from Google Street View (GSV) images along user-selected routes for low-cost, volumetric and quick establishment of the traffic sign infrastructural database that may be associated with Google Maps. The framework and techniques employed in the proposed system are described.

scholarly journals Traffic sign Detection using CNN

2021 ◽  
Vol 10 (3) ◽  
pp. 129-135
Author(s):  
K. Mirunalini ◽  
Vasantha Kalyani David
Keyword(s):  
Signal To Noise Ratio ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Traffic Sign ◽  
Straight Line ◽  
Essential Components ◽  
Sign Detection ◽  
Important Challenge ◽  
Bad Weather ◽  
Traffic Sign Detection

Lane Detection and Traffic sign detection are the essential components in ADAS .Although there has been significant quantity of analysis dedicated to the detection of lane detection and sign detection in the past, there is still need robustness in the system. An important challenge in the current algorithm is to cope with the bad weather and illumination. In this paper proposes an improved Hough transform algorithm in order to achieve detection of straight line while for the detection of curved sections, the tracking algorithm is studied. The proposed method uses Hybrid KSVD for removing the noise and Hybrid Lane Detection Algorithm is used for identifying the lanes and CNN based approach is used for the Traffic sign Detection. The proposed method offers better Peak Signal to Noise Ratio (PSNR) and Root Mean Square (RMS) in contrast to the existing methods.

Traffic sign detection based on improved faster R-CNN for autonomous driving

2022 ◽  
Author(s):  
Xiaomei Li ◽  
Zhijiang Xie ◽  
Xiong Deng ◽  
Yanxue Wu ◽  
Yangjun Pi
Keyword(s):  
Autonomous Driving ◽  
Traffic Sign ◽  
Sign Detection ◽  
Traffic Sign Detection

scholarly journals Long-Range, High-Resolution Camera Optical Design for Assisted and Autonomous Driving

Photonics ◽  
2019 ◽  
Vol 6 (2) ◽  
pp. 73 ◽  
Author(s):  
Furkan Sahin
Keyword(s):  
High Resolution ◽  
Long Range ◽  
Material Selection ◽  
Optical Design ◽  
Autonomous Driving ◽  
Operating Conditions ◽  
Optimized Design ◽  
Traffic Sign ◽  
The Road ◽  
Sign Recognition

High-quality cameras are fundamental sensors in assisted and autonomous driving. In particular, long-range forward-facing cameras can provide vital information about the road ahead, including detection and recognition of objects and early hazard warning. These automotive cameras should provide high-resolution images consistently under extreme operating conditions of the car for robust operation. This paper aims to introduce the design of fixed-focus, passively athermalized lenses for next-generation automotive cameras. After introducing an overview of essential and desirable features of automotive cameras and state-of-the-art, based on these features, two different camera designs that can achieve traffic sign recognition at 200 m distance are presented. These lenses are designed from scratch, with a unique design approach that starts with a graphical lens material selection tool and arrives at an optimized design with optical design software. Optical system analyses are performed to evaluate the lens designs. The lenses are shown to accomplish high contrast from − 40 °C to 100 °C and allow for a 4 × increase in resolution of automotive cameras.

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