vehicle speed estimation
94783902
A Novel Motion Plane Based Approach to Vehicle Speed Estimation
Speed limit violation by vehicles is one of the most frequent reasons for road crashes, which take the lives of many people every year, resulting in an increasing demand for video based vehicle speed estimation systems. One of the biggest challenges to achieve monocular video based vehicle speed estimation is the projection displacement difference (PDD) problem, where there is a plane disparity between street level indicators and above plane feature points of vehicles, thus resulting in unreliable speed estimates. In this paper, a novel motion plane based approach for vehicle speed estimation is proposed, which addresses the problem of PDD. In the proposed method, we consider the center of a vehicle license plate as the vehicle reference point and estimate the hypothetical plane (named motion plane) on which license plate moves. Subsequently, the plate position is mapped on the motion plane and the displacement is then calculated, thus mitigating the effects of PPD. To estimate the motion plane, a texture based shape from template technique is used. Unlike existing methods, the proposed method needs neither to apply any indicator nor to use any information about extrinsic parameters of the camera. Furthermore, since all the license plates are approximately located on a flat plane, the motion plane can be estimated by using several extracted 3 D points. Experimental results show that the proposed method performs better than the state of the art vehicle speed estimation methods, illustrating the efficacy of this approach for achieving reliable vehicle speed estimation.
IEEE Transactions on Intelligent Transportation Systems
Mahmoud Famouri Zohreh Azimifar Alexander Wong
117166847
Energy consumption estimation integrated into the Electric Vehicle Routing Problem
Abstract When planning routes for fleets of electric commercial vehicles, it is necessary to precisely predict the energy required to drive and plan for charging whenever needed, in order to manage their driving range limitations. Although there are several energy estimation models available in the literature, so far integration with Vehicle Routing Problems has been limited and without demonstrated accuracy. This paper introduces the Two stage Electric Vehicle Routing Problem (2sEVRP) that incorporates improved energy consumption estimation by considering detailed topography and speed profiles. First, a method to calculate energy cost coefficients for the road network is outlined. Since the driving cycle is unknown, the model generates an approximation based on a linear function of mass, as the latter is only determined while routing. These coefficients embed information about topography, speed, powertrain efficiency and the effect of acceleration and braking at traffic lights and intersections. Secondly, an integrated two stage approach is described, which finds the best paths between pairs of destinations and then finds the best routes including battery and time window constraints. Energy consumption is used as objective function including payload and auxiliary systems. The road cost coefficients are aggregated to generate the path cost coefficients that are used in the routing problem. In this way it is possible to get a proper approximation of the complete driving cycle for the routes and accurate energy consumption estimation. Lastly, numerical experiments are shown based on the road network from Gothenburg Sweden. Energy estimation is compared with real consumption data from an all electric bus from a public transport route and with high fidelity vehicle simulations. Routing experiments focus on trucks for urban distribution of goods. The results indicate that time and energy estimations are significantly more precise than existing methods. Consequently the planned routes are expected to be feasible in terms of energy demand and that charging stops are properly included when necessary.
Transportation Research Part D: Transport and Environment
Rafael Basso Balazs Kulcsar Bo Egardt Peter Lindroth Ivan D Sanchez-Diaz
92991009
Vehicle Speed Estimation using Image Processing
Article history: Received 5 October 2018 Received in revised form 4 December 2018 Accepted 8 December 2018 Available online 5 January 2019 This paper aims at determining vehicle speed which is necessary for traffic surveillance systems. These systems are very much useful to monitor and manage various traffic conditions such as traffic management, prevention of accident, also secure transportation. This paper describes a comprehensive approach to localizing target vehicles in video under various environmental conditions. The extracted geometry features from the video are continuously projected onto a profile and are constantly tracked. We rely on temporal information of features and their motion behaviors for vehicle identification, which compensates for the complexity in recognizing vehicle shapes, colors, and types. In speed determination vehicle detection and vehicle tracking are the key steps. To overcome the disadvantages of traditional methods, here, vehicle speed determination using image processing is done with Python language and OpenCV library. In this paper, the proposed method consists of few basic steps background subtractions, feature extraction, vehicle tracking etc. The speed is determined using distance travelled by vehicle over number of frames and frame rate.
Fatima Afifah Sharmin Nasrin Abdul Mukit
54460247
SAVeD: Acoustic Vehicle Detector with Speed Estimation capable of Sequential Vehicle Detection
In the ITS (intelligent transportation system) vehicle detection is one of the core technologies. We are developing an acoustic vehicle detector that detects vehicles using a sound map, which is a map of sound arrival time difference on two microphones. We developed vehicle detection algorithms based on state machine and DTW (dynamic time warping) to detect S curves on a sound map drawn by passing vehicles. However, the detection algorithms often fail to detect simultaneous and sequential passing vehicles. This paper presents SAVeD, a sequential acoustic vehicle detector. The SAVeD fits an S curve model to sound map points using a RANSAC (random sample consensus) robust estimation method to detect each vehicle. The SAVeD then removes sound map points corresponding to the detected vehicle and continues vehicle detection process for the following vehicles. Experimental evaluations demonstrated that the SAVeD improves detection accuracy by more than 10 points compared to the state machine based algorithm.
2018 21st International Conference on Intelligent Transportation Systems (ITSC)
Shigemi Ishida Jumpei Kajimura Masato Uchino Shigeaki Tagashira Akira Fukuda
69339528
ViLDAR Visible Light Sensing Based Speed Estimation Using Vehicle Headlamps
The introduction of light emitting diodes (LED) in automotive exterior lighting systems provides opportunities to develop viable alternatives to conventional communication and sensing technologies. Most of the advanced driver assist and autonomous vehicle technologies are based on Radio Detection and Ranging (RADAR) or Light Detection and Ranging (LiDAR) systems that use radio frequency or laser signals, respectively. While reliable and real time information on vehicle speeds is critical for traffic operations management and autonomous vehicles safety, RADAR or LiDAR systems have some deficiencies especially in curved road scenarios where the incidence angle is rapidly varying. In this paper, we propose a novel speed estimation system so called the Visible Light Detection and Ranging (ViLDAR) that builds upon sensing visible light variation of the vehicle's headlamp. We determine the accuracy of the proposed speed estimator in straight and curved road scenarios. We further present how the algorithm design parameters and the channel noise level affect the speed estimation accuracy. For wide incidence angles, the simulation results show that the ViLDAR outperforms RADAR/LiDAR systems in both straight and curved road scenarios.
IEEE Transactions on Vehicular Technology
Hisham Abuella Farshad Miramirkhani Sabit Ekin Murat Uysal Samir A Ahmed
51619876
Practical Methods for Vehicle Speed Estimation Using a Microprocessor Embedded System with AMR Sensors
The proper operation of computing resources in a microprocessor embedded system plays a key role in reducing computing time. Processing the variable amount of collected data in real time improves the performance of a microprocessor embedded system. In this regard, a vehicle's speed measurement system is no exception. The computing time for evaluating any speed value is expected to be reduced as much as possible. Four computational methods, including cross correlation, are discussed. An exemplary pair of recorded signals presenting the change in magnetic field magnitude is analyzed. The sample delay values are compared. The results of the evaluated speed and the execution time of the program code are presented for each method based on a dataset of 200 randomly driven vehicles. The results of the performed tests confirm that the cross correlation based methods are not always reliable in situations when the sample size is small, i.e. it is a segment of the impulse response caused by a driving vehicle.
Sensors
Vytautas Markevicius Dangirutis Navikas Adam Idzkowski Darius Andriukaitis Algimantas Valinevicius Mindaugas Zilys
53512560
A Semi Automatic 2D Solution for Vehicle Speed Estimation from Monocular Videos
In this work, we present a novel approach for vehicle speed estimation from monocular videos. The pipeline consists of modules for multi object detection, robust tracking, and speed estimation. The tracking algorithm has the capability for jointly tracking individual vehicles and estimating velocities in the image domain. However, since camera parameters are often unavailable and extensive variations are present in the scenes, transforming measurements in the image domain to real world is challenging. We propose a simple two stage algorithm to approximate the transformation. Images are first rectified to restore affine properties, then the scaling factor is compensated for each scene. We show the effectiveness of the proposed method with extensive experiments on the traffic speed analysis dataset in the NVIDIA AI City challenge. We achieve a detection rate of 1.0 in vehicle detection and tracking, and Root Mean Square Error of 9.54 (mph) for the task of vehicle speed estimation in unconstrained traffic videos.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Amit Kumar Pirazh Khorramshahi Wei-An Lin Prithviraj Dhar Jun-Cheng Chen Rama Chellappa
53393215
Vehicle Tracking and Speed Estimation from Traffic Videos
The rapid recent advancements in the computation ability of everyday computers have made it possible to widely apply deep learning methods to the analysis of traffic surveillance videos. Traffic flow prediction, anomaly detection, vehicle re identification, and vehicle tracking are basic components in traffic analysis. Among these applications, traffic flow prediction, or vehicle speed estimation, is one of the most important research topics of recent years. Good solutions to this problem could prevent traffic collisions and help improve road planning by better estimating transit demand. In the 2018 NVIDIA AI City Challenge, we combine modern deep learning models with classic computer vision approaches to propose an efficient way to predict vehicle speed. In this paper, we introduce some state of the art approaches in vehicle speed estimation, vehicle detection, and object tracking, as well as our solution for Track 1 of the Challenge.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Shuai Hua Manika Kapoor D Anastasiu
53713612
Single Camera and Inter Camera Vehicle Tracking and 3D Speed Estimation Based on Fusion of Visual and Semantic Features
Tracking of vehicles across multiple cameras with nonoverlapping views has been a challenging task for the intelligent transportation system (ITS) It is mainly because of high similarity among vehicle models, frequent occlusion, large variation in different viewing perspectives and low video resolution. In this work, we propose a fusion of visual and semantic features for both single camera tracking (SCT) and inter camera tracking (ICT) Specifically, a histogram based adaptive appearance model is introduced to learn long term history of visual features for each vehicle target. Besides, semantic features including trajectory smoothness, velocity change and temporal information are incorporated into a bottom up clustering strategy for data association in each single camera view. Across different camera views, we also exploit other information, such as deep learning features, detected license plate features and detected car types, for vehicle re identification. Additionally, evolutionary optimization is applied to camera calibration for reliable 3D speed estimation. Our algorithm achieves the top performance in both 3D speed estimation and vehicle re identification at the NVIDIA AI City Challenge 2018.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Zheng Tang Gaoang Wang Hao Xiao Aotian Zheng Jenq-Neng Hwang
53354823
Speed Estimation and Abnormality Detection from Surveillance Cameras
Motivated by the increasing industry trends towards autonomous driving, vehicles, and transportation we focus on developing a traffic analysis framework for the automatic exploitation of a large pool of available data relative to traffic applications. We propose a cooperative detection and tracking algorithm for the retrieval of vehicle trajectories in video surveillance footage based on deep CNN features that is ultimately used for two separate traffic analysis modalities: (a) vehicle speed estimation based on a state of the art fully automatic camera calibration algorithm and (b) the detection of possibly abnormal events in the scene using robust optical flow descriptors of the detected vehicles and Fisher vector representations of spatiotemporal visual volumes. Finally we measure the performance of our proposed methods in the NVIDIA AI CITY challenge evaluation dataset.
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Panagiotis Giannakeris Vagia Kaltsa Konstantinos Avgerinakis Alexia Briassouli Stefanos Vrochidis Yiannis Kompatsiaris