Inter-vehicle Distance Measurements Research Articles

Stop-Line-Aided Cooperative Positioning for Connected Vehicles

This paper develops a stop-line-aided cooperative positioning framework for connected vehicles, which creatively utilizes the location of the stop-line to achieve the positioning enhancement for a vehicular ad-hoc network (VANET) in intersection scenarios via Vehicle-to-Vehicle (V2V) communication. Firstly, a self-positioning correction scheme for the first stopped vehicle is presented, which applies the stop line information as benchmarks to correct the GNSS/INS positioning results. Then, the local observations of each vehicle are fused with the position estimates of other vehicles and the inter-vehicle distance measurements by using an extended Kalman filter (EKF). In this way, the benefits of the first stopped vehicle are extended to the whole VANET. Such a cooperative inertial navigation (CIN) framework can greatly improve the positioning performance of the VANET. Finally, experiments in Beijing show the effectiveness of the proposed stop-line-aided cooperative positioning framework.

Multiple-Vehicle Localization Using Maximum Likelihood Kalman Filtering and Ultra-Wideband Signals

In this article we study the problem of localizing a fleet of vehicles in an indoor environment using ultra-wideband (UWB) signals. This is typically done by placing a number of UWB anchors with respect to which vehicles measure their distances. The localization performance is usually poor in the vertical axis, due to the fact that anchors are often placed at similar heights. To improve accuracy, we study the use of inter-vehicle distance measurements. These measurements introduce a technical challenge, as this requires the joint estimation of positions of all vehicles, and currently available methods become numerically complex. To go around this, we use a recently proposed technique called maximum likelihood Kalman filtering (MLKF). We present experiments using real data, showing how the addition of inter-vehicle measurements improves the localization accuracy by about 60%. Experiments also show that the MLKF achieves a localization error similar to the best among available methods, while requiring only about 20% of computational time.

Distance measurement system for autonomous vehicles using stereo camera

Abstract The focus of this paper is inter-vehicles distance measurement which is a very important and challenging task in image processing domain. Where it is used in several systems such as Driving Safety Support Systems (DSSS), autonomous driving and traffic mobility. In the current paper, we propose an inter-vehicle distance measurement system for self-driving based on image processing. The proposed system uses two cameras mounted as one stereo camera, in the hosting vehicle behind the rear-view mirror. The detection of vehicles is performed first in a single camera using a recent powerful work from the literature. Then, the same vehicle is detected in the image captured by the second camera using template matching technique. Thus, the inter-vehicle distance is calculated using a simple method based on the position of the vehicle in both cameras, geometric derivations and additional technical data such as distance between the cameras and some other specific angles (e.g. the cameras view field angle). The results of the extensive experiments showed the high accuracy of the proposed method compared to the previous works from literature and it allows to measure efficiently the distances between the vehicles and the hosting vehicle. In addition, this method could be used in several systems of various domains in real time regardless of the object types. The experiments results were done on a Hardware Processor System (HPS) located in a VEEK-MT2S provided by TERASIC.

Bayesian Cooperative Localization With NLOS and Malicious Vehicle Detection in GNSS-Challenged Environments

Owing to the inter-vehicle non-line-of-sight (NLOS) measurement and malicious attack in global navigation satellite system (GNSS) challenged environment, the vehicle position precision is seriously damaged. In order to improve the vehicle position accuracy, we propose a new Bayesian cooperative localization scheme which tackles this problem by combining the vehicle position measurements and inter-vehicle distance measurements. In the proposed scheme, an abnormal vehicle detection algorithm (AVDA) is presented to eliminate the impacts of NLOS and malicious attack. Simulation results demonstrate that the proposed scheme can achieve excellent localization performance in the presence of NLOS and malicious attacks. Based on these results, the abnormal and normal detection rates of AVDA are approximate and the root mean square error (RMSE) is reduced to the sub-meter level. The performances of the proposed scheme are also verified in real environmental conditions by using the simulation of urban mobility (SUMO).

Intervehicle distance estimation through camera images

Vehicles have evolved into synthetic engineering systems owing to the developments in mechanical, electronic, and information technologies. Recently, driving assistance systems have gained considerable attention and are installed in vehicles under development by manufacturers. An important technology for the driving assistance systems is intervehicle distance measurement, for which several technologies have been proposed through hardware and software developments. The intervehicle distance measurement generally requires auxiliary sensor systems, complex algorithms, and high costs. An intervehicle distance estimation method is proposed, which calculates the distance using images obtained through a camera installed in the vehicle. For calculating the distance, the proposed method does not need any other physical sensors or information about the roads on which the vehicle moves. For estimating the intervehicle distance, the proposed method takes advantage of the fact that the width between two adjacent lanes on the road is constant. The difference in the distance calculated using the proposed method and the actual distance is <7 % .

Selective capture based high-speed optical vehicular signaling system

The present article introduces a high-speed optical vehicular signaling system using a distinct and unique capturing strategy called selective capture (SC). The proposed scheme performs the SC within the frame while capturing, not after the frames are captured, thereby reducing the capturing area and thus increasing the camera capture speed. One of the major challenges in optical camera communication (OCC) based vehicular signaling system is low data transmission rate, due to a low sampling rate of a camera receiver, compared with very high-speed modulation of light emitting diodes (LEDs) used as the transmitter. We propose this SC-based technique for a high-speed optical vehicular signaling system. Experiments are conducted to verify the proposed scheme while analyzing various parameters such as margin setting to calculate a total SC area, considering the movement of a vehicle along the roadside, inter-vehicle distance measurement and LED detection from a Raspberry Pi camera (RaspiCam) module. The SC is performed using template matching technique on the RaspiCam module and then the margin is added with the SC to obtain the total SC area. This total SC area is utilized for capturing the taillights of the leading vehicle (LV). The optical vehicle-to-vehicle (V2V) signaling system utilizes two 4 × 4 red LED arrays mounted on LV as the taillights and a RaspiCam mounted on the following vehicle (FV). It is found that the use of RaspiCam set with the total SC area to capture the taillights significantly increases the capture speed of RaspiCam from 120 frames per second (fps) to 435 fps, yielding an efficient and high-speed V2V signaling with flicker-free taillights. Experiment results demonstrate that the proposed optical V2V provides a data rate of up to 6.912 kbps and achieves acceptable bit error rate performance at a distance of up to 175 cm.

A New Decentralized Bayesian Approach for Cooperative Vehicle Localization Based on Fusion of GPS and VANET Based Inter-Vehicle Distance Measurement

Accurate and reliable vehicle localization is a key component of numerous automotive and Intelligent Transportation System (ITS) applications, including active vehicle safety systems, real time estimation of traffic conditions, and high occupancy tolling [1]. Various safety critical vehicle applications in particular, such as collision avoidance or mitigation, lane change management or emergency braking assistance systems, rely principally on the accurate and reliable knowledge of vehicles? positioning within given vicinity.

Accurate inter-vehicle distance measurement based on monocular camera and line laser

Accurate inter-vehicle distance measurement based on monocular camera and line laser

Distributed control of angle-constrained cyclic formations using bearing-only measurements

This paper studies distributed control of multi-vehicle formations with angle constraints using bearing-only measurements. It is assumed that each vehicle can only measure the local bearings of their neighbors and there are no wireless communications among the vehicles. The desired formation is a cyclic one, whose underlying information flow is described by an undirected cycle graph. We propose a distributed bearing-only formation control law that ensures local exponential or finite-time stability. Collision avoidance between any vehicles can be locally guaranteed in the absence of inter-vehicle distance measurements.

Cooperative GNSS-based method for vehicle positioning

A very promising method for coping with the increasing demands on position accuracy for vehicular networks is cooperative positioning. Instead of adding more and more sensors in a self-contained system this technique aims exploiting information which already exists. This paper addresses a new cooperative GNSS positioning approach which is referred to as Advanced Shared Pseudorange (ASP) method. It customizes broadcasted GNSS pseudorange measurements of collaborating vehicles in a way that they can be applied in standard positioning algorithms. Specifically, in urban environments the increased number of usable satellites improves position accuracy or even makes GNSS position estimation possible in the first place. Moreover, the adjustment of pseudoranges from collaborating vehicles only requires inter-vehicle distance measurements as provided by radio ranging techniques. Thereby hardware costs are kept to a minimum. The capability of the new approach is emphasized by a comparison with other cooperative algorithms and test results of a loosely coupled GNSS/INS position estimation for a simulated urban road-segment scenario.