Sensing for HOV/HOT Lanes Enforcement (original) (raw)
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High Occupancy Vehicle Detection
Lecture Notes in Computer Science, 2008
High occupancy vehicles lanes (HOV) are highway lanes usually reserved to vehicles carrying at least two persons. They are designed to help move more people though congested areas. In this context, automatic passenger counting systems could be useful to grant access to or to control vehicles in those lanes. In this work, we propose a real-time passenger detection system based on the analysis of visual images. Each person is detected by mixing the information from different types of classifiers in order to make the detection process faster and more robust.
A Laser Curtain for Detecting Heterogeneous Lane-less Traffic
2019 11th International Conference on Communication Systems & Networks (COMSNETS), 2019
Traffic management system plays a vital role in the present smart city applications. In developing countries, heterogeneous lane-less traffic management is a significant challenge. This paper proposes a dual-purpose laser-based sensor configuration for vehicle classification. It classifies heterogeneous and lesslane disciplined traffic based on vehicles width. This work also suggests a methodology for the vehicle to infrastructure (V2I) communication using visible light. Index Terms-Vehicle detector, Intelligent transportation, VLC, V2I Communication. I. INTRODUCTION The majority of individuals in developing economies are preferred to travel with own mode of transportation due to the rise in quality of urban lifestyle [1], which leads to an increase in traffic congestion. To solve the traffic congestion and improve the capacity two methods are in use, one is to expand the road infrastructure and second is to use a traffic management system (TMS) [2]. The capacity of road infrastructure in developing countries have not increased as par with the traffic, so adopting a TMS provides a feasible solution. The traffic sensor is an essential component in TMS, which detects the number of vehicles, types of vehicles, speed, etc. At present, most of the available sensors are for disciplined and homogenous traffic. Only a few traffic counter/detectors are available for monitoring the heterogeneous and lane-less traffic. Each of them has its advantages and limitations. Sheik Mohammed Ali et al. [3] [4] proposed an inductive loop structure which detects different classes of vehicles such as two-wheelers, cars, auto rickshaw, bus, etc. For single-lane traffic Aravind et al. [5] proposed a vehicle classifier system based on optical beam interruption, where laser light was projected across the road such that vehicles interrupt the light beam; with this, they estimated the vehicle tire size, speed, and distance between axles. This system works better for singlelane traffic and may not be suitable for multi-lane traffic. Mallikarjuna et al. [6] modeled a traffic classifier with video and image processing technique. In this system performance limited to weather and lighting conditions. Samer Rajab et al. [7] designed a configuration for classifying the vehicles using the piezoelectric sensor. The sensor
VPDS: An AI-Based Automated Vehicle Occupancy and Violation Detection System
Proceedings of the AAAI Conference on Artificial Intelligence, 2019
High Occupancy Vehicle/High Occupancy Tolling (HOV/HOT) lanes are operated based on voluntary HOV declarations by drivers. A majority of these declarations are wrong to leverage faster HOV lane speeds illegally. It is a herculean task to manually regulate HOV lanes and identify these violators. Therefore, an automated way of counting the number of people in a car is prudent for fair tolling and for violator detection.In this paper, we propose a Vehicle Passenger Detection System (VPDS) which works by capturing images through Near Infrared (NIR) cameras on the toll lanes and processing them using deep Convolutional Neural Networks (CNN) models. Our system has been deployed in 3 cities over a span of two years and has served roughly 30 million vehicles with an accuracy of 97% which is a remarkable improvement over manual review which is 37% accurate. Our system can generate an accurate report of HOV lane usage which helps policy makers pave the way towards de-congestion.
Automatic detection of vehicle passengers through near-infrared fusion
1999
W e undertook a study to determine if the automatic detection and counting of vehicle passengers is feasible. An automated passenger counting system would greatly facilitate the operation of freeway lanes reserved for buses, car-pools, and emergency vehicles (HO V lanes). In the present paper we report our findings regarding the appropriate sensor phenomenology and arrangement for the task. W e propose a novel system based o n fusion of near-infrared imaging signals and we demonstrate its adequacy with theoretical and experimental arguments.
Vehicle Driver Warning Systems Using Road Marking and Traffic Light Detection
https://www.ajer.org/current-issue.html, 2022
Everyone has experienced fatigue and sleepiness while driving. This makes him not know the direction so that it violates traffic and can cause an accident. Violations that usually occur are breaking through traffic lights and violating road markings. Therefore, a simulation software was made to help negligent and sleepy drivers not to violate traffic and reduce accidents. The technology used is image processing with C# programming and the EmguCV library using the Haar Cascade Classifier and Color Detection methods. Haarlike features are rectangular features, which give a specific indication of an image. The captured image will be processed in two stages, namely preprocessing to detect markings and Gaussian filter to detect traffic lights. The results of the preprocessing will be processed in the Haar Cascade Classifier to get the ROI of the marker and then look for the coordinates to find the distance between the marker and the driver. The limit used in measuring distance is 25.57 cm (85 pixels). If the coordinate distance is less than 25.57 cm, the alarm will sound and alert the driver to stay away from the marker and if the coordinate distance is more than 25.57 cm, the alarm will be off. While the results of the gaussian filter will be converted into HSV frames to detect red and green colors using the color pixel values of each color. The color of the light can be detected when the contour size value is between 0 and 6.
A review of automotive infrared pedestrian detection techniques
IET Irish Signals and Systems Conference (ISSC 2008), 2008
In automotive design, the issue of safety remains a growing priority. Recently the focus has extended beyond the occupants of the vehicle and has turned towards other Vulnerable Road Users (VRU). Simple night vision systems have already become an important safety feature in modern high end automobiles. The next generation of advanced driver assistance systems will automate the detection of VRUs, to improve safety further by not distracting the driver's attention from the road ahead, and even identifying dangerous situations where the driver may not. This paper presents a review of the state of the art image processing techniques for automatic detection and classification of VRUs in automotive far infrared imagery.
Automatic detection of vehicle occupants: the imaging problemand its solution
Machine Vision and Applications, 2000
The automatic detection and counting of vehicle occupants is a challenging research problem that was given little attention until recently. An automated vehicle-occupant-counting system would greatly facilitate the operation of freeway lanes reserved for car pools (high occupancy vehicle lanes or HOV lanes). There are three major aspects of this problem: (a) the imaging aspect (sensor phenomenology), (b) the pattern recognition aspect, and (c) the system architecture aspect. In this paper, we present a solution to the imaging aspect of the problem. We propose a novel system based on fusion of near-infrared imaging signals and we demonstrate its adequacy with theoretical and experimental arguments. We also compare our solution to other possible solutions across the electromagnetic spectrum, particularly in the thermal infrared and visible regions.
2017
Due to increase of vehicle usage all around the world, the importance of safety driving in traffic is increasing. All of the countries around the world are taking actions to increase the safety driving habitats and decrease the number oftraffic accidents. One of the applied precautions is to put necessary automatic auditing mechanisms into service for controlling the drivers as they drive since reckless drivers may not obey many traffic rules. In this study, image andvideo processing based methods are applied to identify the dangerously lane changing vehicles/drivers in the traffic. The proposed method focuses on to detect three different violations in traffic: the vehicles frequently changingtraffic lanes, the vehicles changing lanes when it is forbidden, and the vehicles overtaking the other vehicles using the right lanes instead of left one. The proposed method is based on the image and video processing techniques. Itfirst detects the vehicles in video sequences, then tracks ...
Imaging sensor technology for intelligent vehicle active safety and driver assistant systems
International Journal of Vehicle Autonomous Systems, 2012
Vehicular Active Safety and Driver Assistant Systems (ASDASs) rely heavily on sensors for achieving their goal of protecting the driver and passengers from potentially dangerous situations. The list of such sensors includes imaging sensors operating in different wavelength bands of the visible (i.e., video cameras) and IR spectrum, as well as ranging sensors such as ultrasonic, radar and lidar. The non-imaging ranging sensors are useful for applications that do not require object recognition/classifi cation or scene understanding, but they generally have poor angular resolution and do not provide much information on the spatial characteristics of objects, making object recognition or classifi cation and lane following diffi cult or impossible using such sensors alone. On the other hand, inexpensive vision sensors can capture the scene image in high spatial resolution and a wide fi eld of view, which makes them ideal for object recognition and lane following under most conditions. The objective of this 'Over the Horizon' (OTH) sensor technology overview is to explore emerging imaging sensor technologies that can lead to signifi cant capability improvement as well as cost reduction for future automotive driver assistance and active safety systems. The technologies covered include visible, IR and hyperspectral imaging systems. We also discuss 3D imaging systems. We provide a summary description of different sensors/systems, system architecture and implementation, as well as cost/performance tradeoffs, technology gaps, deployment scenarios and technology trends in the near-, mid-and long-term.