Stereo Vision-Based Road Debris Detection System for Advanced Driver Assistance Systems

2021 ◽  
Vol 10 (1) ◽  
Author(s):  
Naveen Kumar Bangalore Ramaiah ◽  
◽  
Subrata Kumar Kundu ◽  
Keyword(s):  
Stereo Vision ◽  
Detection System ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Detection Algorithm ◽  
Potential Threat ◽  
Safe Driving ◽  
Collision Warning ◽  
On The Road ◽  
3D Information

Reliable detection of obstacles around an autonomous vehicle is essential to avoid potential collision and ensure safe driving. However, a vast majority of existing systems are mainly focused on detecting large obstacles such as vehicles, pedestrians, and so on. Detection of small obstacles such as road debris, which pose a serious potential threat are often overlooked. In this article, a novel stereo vision-based road debris detection algorithm is proposed that detects debris on the road surfaces and estimates their height accurately. Moreover, a collision warning system that could warn the driver of an imminent crash by using 3D information of detected debris has been studied. A novel feature-based classifier that uses a combination of strong and weak features has been developed for the proposed algorithm, which identifies debris from selected candidates and calculates its height. 3D information of detected debris and vehicle’s speed are used in the collision warning system to warn the driver to safely maneuver the vehicle. The performance of the proposed algorithm has been evaluated by implementing it on a passenger vehicle. Experimental results confirm that the proposed algorithm can successfully detect debris of ≥5 cm height for up to a 22 m distance with an accuracy of 90%. Moreover, the debris detection algorithm runs at 20 Hz in a commercially available stereo camera making it suitable for real-time applications in commercial vehicles.

A Novel Method for Tramcar Anti-Collision Warning System Based on Monocular Vision

2012 ◽  
Vol 430-432 ◽  
pp. 1871-1876
Author(s):  
Hui Bo Bi ◽  
Xiao Dong Xian ◽  
Li Juan Huang
Keyword(s):  
Software Design ◽  
Early Warning System ◽  
Warning System ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Monocular Vision ◽  
System Construction ◽  
Collision Warning ◽  
Lane Width ◽  
Novel Method

For the problem of tramcar collision accident in coal mine underground, a monocular vision-based tramcar anti-collision warning system based on ARM and FPGA was designed and implemented. In this paper, we present an improved fast lane detection algorithm based on Hough transform. Besides, a new distance measurement and early-warning system based on the invariance of the lane width is proposed. System construction, hardware architecture and software design are given in detail. The experiment results show that the precision and speed of the system can satisfy the application requirement.

scholarly journals Empirical Analysis of Safe Distance Calculation by the Stereoscopic Capturing and Processing of Images Through the Tailigator System

Sensors ◽  
2019 ◽  
Vol 19 (22) ◽  
pp. 5044
Author(s):  
Gerd Christian Krizek ◽  
Rene Hausleitner ◽  
Laura Böhme ◽  
Cristina Olaverri-Monreal
Keyword(s):  
Low Cost ◽  
Field Tests ◽  
Warning System ◽  
Collision Warning ◽  
The Road ◽  
Cost System ◽  
Straight Line ◽  
Real Value ◽  
On The Road ◽  
Forward Collision Warning

Driver disregard for the minimum safety distance increases the probability of rear-end collisions. In order to contribute to active safety on the road, we propose in this work a low-cost Forward Collision Warning system that captures and processes images. Using cameras located in the rear section of a leading vehicle, this system serves the purpose of discouraging tailgating behavior from the vehicle driving behind. We perform in this paper the pertinent field tests to assess system performance, focusing on the calculated distance from the processing of images and the error margins in a straight line, as well as in a curve. Based on the evaluation results, the current version of the Tailigator can be used at speeds up to 50 km per hour without any restrictions. The measurements showed similar characteristics both on the straight line and in the curve. At close distances, between 3 and 5 m, the values deviated from the real value. At average distances, around 10 to 15 m, the Tailigator achieved the best results. From distances higher than 20 m, the deviations increased steadily with the distance. We contribute to the state of the art with an innovative low-cost system to identify tailgating behavior and raise awareness, which works independently of the rear vehicle’s communication capabilities or equipment.

scholarly journals Road Segmentation using Semantic Segmentation Networks for ADAS

2019 ◽  
Vol 8 (11) ◽  
pp. 1740-1743
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Warning System ◽  
Semantic Segmentation ◽  
Boundary Line ◽  
Segmentation Result ◽  
Safe Driving ◽  
The Road ◽  
Straight Line ◽  
Road Segmentation

In this paper, we propose a method to automatically segment the road area from the input road images to support safe driving of autonomous vehicles. In the proposed method, the semantic segmentation network (SSN) is trained by using the deep learning method and the road area is segmented by utilizing the SSN. The SSN uses the weights initialized from the VGC-16 network to create the SegNet network. In order to fast the learning time and to obtain results, the class is simplified and learned so that it can be divided into two classes as the road area and the non-road area in the trained SegNet CNN network. In order to improve the accuracy of the road segmentation result, the boundary line of the road region with the straight-line component is detected through the Hough transform and the result is shown by dividing the accurate road region by combining with the segmentation result of the SSN. The proposed method can be applied to safe driving support by autonomously driving the autonomous vehicle by automatically classifying the road area during operation and applying it to the road area departure warning system

scholarly journals Vision-Based Lane Line Detection System

2021 ◽  
Vol 09 (06) ◽  
Author(s):  
Poorna Vishwanth ◽  
Keyword(s):  
Automobile Industry ◽  
Detection System ◽  
Warning System ◽  
Vehicle Detection ◽  
Detection Algorithm ◽  
Lane Detection ◽  
Line Detection ◽  
Vertical Edge ◽  
Horizontal Edge ◽  
Driver Assistance

Since the 1990s, the rising key issue of the automobile industry is self-driving or driverless vehicles. Apparently, one of the most important challenges for smart self-driving cars comprises lane-detecting and lane-tracking capability to ensure safety and also decrease vehicle accidents for driver assistance systems. Since road lane detection is one of the most challenging tasks, driverless vehicles must learn to observe the road from a visual perspective in order to achieve automatic driving. Most of the research Works done so far can only detect the lanes or vehicles separately. However, in this paper, we propose a method to combine lane information and vehicle/obstacle information that can support the driver assistance system, driver warning system or the lane change assistant system so that it enhances the quality of results. For the lane changing system, the system detects or tracks the lane lines and detects the vehicles on all sides of a test vehicle. In lane detection, line detection algorithms such as the Canny Edge detection algorithm are used to detect the lane edges. Kalman filter will be used to track the vehicle detected from the vehicle detection algorithm. For vehicle detection, we use Otsu’s thresholding, horizontal edge filtering and vertical edge. The vertical edge filter and the Otsu’s thresholding are used to detect the vehicles on all sides of the test vehicles, then the horizontal edge is used to verify the vehicles detected.

From stixels to asteroids: Towards a collision warning system using stereo vision

2019 ◽  
Vol 2019 (15) ◽  
pp. 34-1-34-7
Author(s):  
Willem P. Sanberg ◽  
Gijs Dubbelman ◽  
Peter H.N. de With
Keyword(s):  
Stereo Vision ◽  
Warning System ◽  
Collision Warning

Implementation of Vehicle Detection System Using WSN

2013 ◽  
Vol 411-414 ◽  
pp. 1459-1464
Author(s):  
Yun Long Li ◽  
Chun Xin Wang ◽  
Xiao Li Zhou ◽  
Huan Juan Wang ◽  
Ya Kun Liu
Keyword(s):  
Road Traffic ◽  
Detection System ◽  
Vehicle Detection ◽  
Intelligent Transportation ◽  
Detection Algorithm ◽  
State Machine ◽  
Sensor Nodes ◽  
Guidance System ◽  
The Road ◽  
On The Road

Vehicle Detection System plays a basic role in the field of intelligent transportation, and is the cornerstone of constructing modern intelligent transportation system. This paper presents a new vehicle detection algorithm using WSN that called the adaptive state machine. The algorithm can adaptively update the threshold and baseline; use the state machine to achieve the aim of the accurate and efficient vehicle detection. It can be used for the detection of road traffic flow, and can be used in large parking vehicle guidance system. On the road, we have deployed 76 Sensor Nodes to evaluate the performance. We observe the accurate of the road vehicle detection rate of vehicle detection system is nearly 98%.

Intelligent Vehicular Warning System for VANET

2011 ◽  
Vol 145 ◽  
pp. 164-168 ◽  
Author(s):  
Yi Ting Mai ◽  
Jeng Yueng Chen ◽  
Yi Kuan Liu ◽  
Wen Yi Lee ◽  
Guan Ting Wu ◽  
...  
Keyword(s):  
Traffic Safety ◽  
Ad Hoc ◽  
Intelligent System ◽  
Warning System ◽  
Control Unit ◽  
Base Station ◽  
Safe Driving ◽  
Warning Message ◽  
V2v Communications ◽  
On The Road

The vehicular ad hoc network (VANET) has made significant progress in recent years, attracting a lot of interest from academia and the industry. VANET involves vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communications based on a wireless network. V2I refers to the communication between vehicles and infrastructure of roadside unit (RSU), e.g., a base station and access point (AP) connected to the Internet. V2V refers to the direct or multi-hop communications among vehicles in VANET. V2V is efficient and cost effective owing to its short range bandwidth advantage and its ad hoc nature. V2V communications are enabling technologies that enhance the driver’s awareness of nearby vehicular traffic, leading to improved traffic safety and efficiency. The V2V mode provides a communications platform between road vehicles (cars, bikes, scooters, motorcycles, trucks, etc.) without requiring a central control unit. Safety-related V2V applications are enabled via an integrated early warning mechanism. To facilitate safe driving, we propose an Intelligent Vehicular Warning System (IVWS) that sends an immediate warning message in the event of an accident. According to V2V communications, the other cars or vehicles could have enough time to avoid the accident and make an appropriate decision such as slow down, stop, and detour after receiving the urgent warning messages. Furthermore, the local CMS (Changeable Message Sign) can show the accident information for neighbor vehicles when receiving the warning message. To achieve experimental architecture with our proposed IVWS, the robot vehicles have been designed to simulate vehicles on the road. Besides, vehicles also apply ZigBee wireless interface to communicate with each other. The experiment has shown that our proposed intelligent system can initially provide message display and safety driving for vehicles when traffic accident occurred.

Realtime On-Road Vehicle Detection Approach Based on Cascaded Structure

2011 ◽  
Vol 130-134 ◽  
pp. 2429-2432
Author(s):  
Liang Xiu Zhang ◽  
Xu Yun Qiu ◽  
Zhu Lin Zhang ◽  
Yu Lin Wang
Keyword(s):  
Warning System ◽  
Vehicle Detection ◽  
Autonomous Driving ◽  
Detection Algorithm ◽  
Detection Accuracy ◽  
Driver Assistance ◽  
Road Vehicle ◽  
Collision Warning ◽  
Detection Approach ◽  
Transportation Applications

Realtime on-road vehicle detection is a key technology in many transportation applications, such as driver assistance, autonomous driving and active safety. A vehicle detection algorithm based on cascaded structure is introduced. Haar-like features are used to built model in this application, and GAB algorithm is chosen to train the strong classifiers. Then, the real-time on-road vehicle classifier based on cascaded structure is constructed by combining the strong classifiers. Experimental results show that the cascaded classifier is excellent in both detection accuracy and computational efficiency, which ensures its application to collision warning system.

scholarly journals UAV Collision Detection Algorithm based on Formulafor Fast Calculation

2021 ◽  
Vol 12 (6) ◽  
pp. 452-456
Author(s):  
Jung Kyu Park Et.al
Keyword(s):  
Collision Detection ◽  
Movement Time ◽  
Warning System ◽  
Detection Algorithm ◽  
Vehicle Safety ◽  
Collision System ◽  
Fast Calculation ◽  
Collision Warning ◽  
Collision Avoidance System ◽  
Forward Collision Warning

Collision Avoidance System (CAS) is known as a pre-collision system or a forward collision warning system, and research has first begun as a vehicle safety system. In this paper, we propose an algorithm for collision detection of UAV. The proposed algorithm uses a mathematical method and detects the collision of the UAV by modeling it in a two-dimensional plane. Using the mathematical modeling method, it is possible to determine the collision location of the UAV in advance. Experiments were conducted to measure the performance and accuracy of the proposed algorithm. In the experiment, we proceeded assuming three environments and were able to detect an accurate collision when the UAV moved. By applying the algorithm proposed in the paper to CAS, many collision accidents can be prevented. The proposed algorithm detects collisions through mathematical calculations. In addition, the movement time of the UAV was modeled in a 2D environment to shorten the calculation time.

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