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Inferring the type of vehicles on a road is a fundamental task within several applications. Some recent works have exploited Global Positioning System (GPS) devices and used classification of GPS traces to tackle the problem. Existing approaches based on GPS data make use of GPS trajectories sampled at high frequency (about 1 sample per second), but GPS trackers currently installed on public and commercial...
Estimation of the energy consumption rate for electric vehicles (EVs) plays a critical role in a variety of EV eco-driving applications and systems. As a result, quite a few studies have been dedicated to the development of estimation models. To improve the predictive and real-time performance of EV energy consumption rate estimation for eco-driving applications, we proposed herein a hybrid modeling...
This paper proposes an approach that predicts the road course from camera sensors lever-aging deep learning techniques. Road pixels are identified by training a multi-scale convolutional neural network on a large number of full-scene-labeled night-time road images including adverse weather conditions. A framework is presented that applies the proposed approach to longer distance road course estimation,...
Autonomous on-road vehicles or vision-based driver assistance benefit from free-space analysis. This paper evaluates the accuracy of free-space detection in stereo and monocular vision on KITTI benchmark data. Such an evaluation of low-level computer vision algorithms is, for example, also necessary as free-space analysis is recently becoming an important module for designing vehicle test beds. The...
In this paper, we present a fast approach for matching stereo images acquired by a stereo sensor embedded in a moving vehicle. The proposed approach exploits the disparity map already computed at the preceding frame to improve the matching results at the current one. An edge association method is used to track the edge curves over time. Local disparity constraints are computed for all the edge points...
As traffic continues to grow up, the issue regarding the road accident also growing quickly. The accident occurred due to the high speed of vehicles on the road. This paper proposed a vehicle speed detection and travel time estimation system using Raspberry Pi to estimate the speed of passing vehicles through this system. The system is designed to detect the moving vehicles and calculate its velocity...
Concerning the increasing demand for intelligent and efficient urban vehicle systems with low cost maintainability and high passenger comfort, reliable methods are needed to model and to evaluate the imposed performances. The measurements, vibrations emerging on the wheels and the body, that has ben taken on a city bus are analyzed in the frequency domain. In this paper a parametric spectral analysis...
Considering the traffic safety in the scenario of arterial road with on-ramp, this study proposes a time-to-collision (TTC) based vehicular collision warning algorithm under connected environment. In particular, the information of vehicles of interest, i.e., position, traveling direction and velocity, is assumed to be collected by the roadside device via the vehicle-to-infrastructure (V2I) communications...
In this paper, the performance of the well-known Generalized Adaptive Smoothing Method (GASM) as online traffic speed estimator with Floating Car Data (FCD) as single source of data is assessed. Therefore, the main challenges originating from the sparseness and delay in collecting FCD are addressed and a procedure using the GASM is proposed that allows estimating traffic velocities continuously. In...
On-road large vehicles are always considered as potential danger, which could generate serious damage and strongly threaten road safety. Hence, large vehicle related accident forecasting becomes an important research topic in intelligence transportation area. In this paper, a large vehicle first clustering method (LVFC) is proposed to estimate the risk level of vehicle group. Composite performance...
To be able to predict the evolution of the driving context, estimate the expected risks and plan future behavior alternatives, it is crucial to know where traffic participants can go and where they will most likely go. Consequently, for future Advanced Driver Assistance Systems (ADAS), precise, lane-accurate localization of the ego- as well as the other vehicles is a key technology. The proposed standard...
Real-time information about the current tyre-road friction coefficient is important for vehicles with increasing levels of automation to adapt intervention thresholds and vehicle velocity. Online friction estimation poses large challenges in terms of availability and accuracy of the estimate. When estimating the friction based on the slip and force of a tyre, the accuracy of the estimate depends on...
An essential function for automated vehicle technologies is accurate localization. It is difficult, however, to achieve lane-level accuracy with low-cost Global Navigation Satellite System (GNSS) receivers due to the biased noisy pseudo-range measurements. Approaches such as Differential GNSS can improve the accuracy, but usually require an enormous amount of investment in base stations. The emerging...
Connected vehicles extend the capability of information collection, and thus open more opportunities for innovative advanced driver assistance systems (ADAS). In this paper, we propose a Lane Speed Monitoring (LSM) application based on vehicle-to-vehicle (V2V) communication. This application takes advantage of Basic Safety Messages (BSM) transmitted from equipped vehicles via dedicated short range...
Many vision-based systems have been proposed for intersection monitoring, but few of them use wide angle cameras. In this paper, we present a fisheye-stereo monitoring system that will be installed especially in rural areas. Our goal is to estimate the extrinsic calibration decoupled into a rotation followed by a translation. In the context of rural intersections, methods that purely rely on the geometry...
This paper proposes an online extrinsic camera calibration system able to determine the pitch and roll angle of a moving, forward-looking camera relative to the road surface. Some road surfaces do not show enough texture to allow reliable optical flow calculation and accurate per-frame pitch and roll estimations, respectively. If inaccurate per-frame estimations are regarded in the final calibration,...
In this paper we present a new lane markers detection and estimation algorithm aiming to improve lane detection methods. We first estimate the area of lane marking using the profile of the lane estimation in a confidence map. After that a fitting method is applied to improve the lane marker detection accuracy. To track our lane markers over time and make the association between two iteration, we use...
The localization of a vehicle is a central task of autonomous driving. Most of the time, it is solved by considering a single algorithm with a few sensors. In this paper, we propose a cooperative fusion architecture based on two main algorithms: a laser-based Simultaneous Localization And Mapping (SLAM) process and a lane detection and tracking approach using a single camera. Both algorithms are designed...
An extensive, precise and robust recognition and modeling of the environment is a key factor for next generations of Advanced Driver Assistance Systems and development of autonomous vehicles. In this paper, a real-time approach for the perception of multiple lanes on highways is proposed. Lane markings detected by camera systems and observations of other traffic participants provide the input data...
This paper demonstrates a framework to optimize the investment of dynamic wireless charging (DWC) infrastructure for charging-in-motion services. The services require DWC infrastructure deployed on public roads to extend battery lifespan and reduce battery sizes while increasing driving range simultaneously. Since it would be financially infeasible to have such investments serving only few vehicles,...
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