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Speed enforcement on public roadways is an important issue in order to guarantee road security and to reduce the number and seriousness of traffic accidents. Traditionally, this task has been partially solved using radar and/or laser technologies and, more recently, using video-camera based systems. All these systems have significant shortcomings that have yet to be overcome. The main drawback of classical Doppler radar technology is that the velocity measurement fails when several vehicles are in the radars beam. Modern radar systems are able to measure speed and range between vehicle and radar. However, this is not enough to discriminate the lane where the vehicle is driving on. The limitation of several vehicles in the beam is overcome using laser technology. However, laser systems have another important limitation: They cannot measure the speed of several vehicles simultaneously. Novel video-camera systems, based on license plate identification, solve the previous drawbacks, but they have the problem that they can only measure average speed but never top-speed. This paper studies the feasibility of using an interferometric linear frequency modulated continuous wave radar to improve top-speed enforcement on roadways. Two different systems based on down-the-road and across-the-road radar configurations are presented. The main advantage of the proposed solutions is they can simultaneously measure speed, range, and lane of several vehicles, allowing the univocal identification of the offenders. A detailed analysis about the operation and accuracy of these solutions is reported. In addition, the feasibility of the proposed techniques has been demonstrated with simulations and real experiments using a Ka-band interferometric radar developed by our research group. [ABSTRACT FROM PUBLISHER]

Robust road-boundary extraction/tracking is one of the main problems in autonomous roadway navigation. Although the road boundary can be defined by various means including lane markings, curbs, and borders of vegetation, this paper focuses on road-boundary tracking using curbs. A vehicle-mounted (downward tilted) 2-D laser-measurement system is utilized to detect the curbs. The tracking problem is difficult because both the vehicle is moving and the target is disappearing, reappearing, and maneuvering in clutter. The interacting-multiple-model probabilistic-data-association filter (IMMPDAF) is proposed to solve the problems after detailed analysis. Track initiation, confirmation, and deletion are performed using the sequential-probability-ratio test. Extensive simulations followed by experiments in a campus environment show that the road-boundary tracking utilizing curbs is possible and robust through IMMPDAF. [ABSTRACT FROM AUTHOR]

Clear and distinctive pavement markings play a critical role in providing traffic information and avoiding traffic crashes at night and in adverse weather conditions. Periodically inspecting pavement marking is essential but requires vast human and material resources currently. This paper attempts to use the low-channel LiDAR sensor to detect and evaluate the stain, wear, and cracks of pavement marking based on the research of Pike and Che that the retroreflectivity of pavement markings is highly correlated with the laser intensity of LiDAR. Thus, the deployment location of mobile LiDAR based on its built-in characteristics and mechanical structure is optimized. An optimization model is formed and solved with the elitist preservation genetic algorithm (EGA). The results show that the best installation height is 0.5 m. The rotation angle of LiDAR is 128.4°, 148.0° in the urban and highway scenarios, respectively. The field test results show that the pavement marking can obtain best the coverage, density, and effective use of LiDAR point cloud with the above setting. The field test’s mean relative error (MRE) is less than 5% and 10% in vertical installation and vertical installation with an inclination, respectively. [ABSTRACT FROM AUTHOR]

A reliable obstacle detection system is crucial for autonomous surface vehicles (ASVs) to realize fully autonomous navigation with no need of human intervention. However, the current detection methods have particular drawbacks, such as poor detection for small objects, low estimation accuracy caused by water surface reflection, and a high rate of false-positive on water-sky interference. Therefore, we propose a new encoder–decoder structured deep semantic segmentation network, which is a water obstacle detection network based on image segmentation (WODIS), to solve the abovementioned problems. The first design feature of WODIS utilizes the use of an encoder network to extract high-level data based on different sampling rates. In order to improve obstacle detection at sea–sky line areas, an attention refine module (ARM) activated by both global average pooling and max pooling to capture high-level information has been designed and integrated into WODIS. In addition, a feature fusion module (FFM) is introduced to help concatenate the multidimensional high-level features in the decoder network. The WODIS is tested and cross-validated using four different types of maritime datasets with the results demonstrating that the mean intersection over union (mIoU) of WODIS can achieve superior segmentation effects for sea-level obstacles to values as high as 91.3%. [ABSTRACT FROM AUTHOR]

Lane information is an essential part of high-resolution micro-traffic data (HRMTD). Most of the lane detection algorithms for Light Detection and Ranging (LiDAR) are applied to high-density onboard LiDAR and airborne LiDAR, which cannot be used directly to process low-density roadside LiDAR data. In this article, an algorithm for lane detection applied to low-density roadside LiDAR is proposed, which includes three steps: ground recognition, lane marking point extraction, and pavement lane marking points clustering. Firstly, an improved algorithm for ground recognition is proposed to get normal ground points (NGPs). Then, the lane marking points are extracted from the no lane marking points based on the difference of laser intensity. Finally, we divided the ground within the range of LiDAR scanning into different stripes, and search the maximum linear density of the stripe to extract the lane markings. The average distance error (ADE) of lane detection results is less than 0.1m by fusing and comparing the test results with the aerial photographs. The proposed algorithm is also proved to be robust in multiple test locations and the results can be used to assist high-precision vehicle positioning in vehicle-to-infrastructure (V2I) cooperation application within the intelligent transportation system. [ABSTRACT FROM AUTHOR]

The previous studies showed that rainy and snowy weather can reduce the quality of LiDAR data. In rainy and snowy weather, laser beams of LiDAR were often blocked by raindrops or snowflakes, which was called weather occlusion. The vehicle detection with weather occlusion is a challenge. When the traditional density-based spatial clustering of applications with noise (DBSCAN) was used for vehicle clustering, the data processing showed that the false detection rate of the conventional DBSCAN under the snowy weather was high. This paper aims to present the characteristics of roadside LiDAR data in snowy and rainy days and improve the accuracy of vehicle detection during challenging weather conditions. A revised DBSCAN method named 3D-SDBSCAN is raised up to distinguish vehicle points and snowflakes in the LiDAR data. Adaptive parameters were applied in the revised DBSCAN method to detect vehicles with different distances from the roadside LiDAR sensor. The performance of the proposed method and the conventional DBSCAN algorithm were compared using the data collected under rainy and snowy conditions. The results showed that the 3D-SDBSCAN algorithm could overcome weather occlusion issue better than the conventional one. [ABSTRACT FROM AUTHOR]

High-resolution micro traffic data are important to traffic safety and efficiency analysis. In this study, a roadside LiDAR sensor is used to collect 3D point clouds of surrounding objects. An automatic background construction and object detection method is proposed on the basis of the operation principle of the LiDAR sensor. In the algorithm, the discrete horizontal and vertical angular values can be considered as coordinates of pixels in digital images, and the farthest and mean distance of each azimuth are used to construct the background dataset. Then, vehicle and pedestrian points are extracted on the basis of the distance difference of each point with the same angular value between the target frame dataset and the background dataset. A density-based spatial clustering method is employed to group points into clusters and identify vehicles and pedestrians automatically. Finally, the performance of the algorithm is tested for roadside LiDAR data preprocessing under different traffic conditions. Our algorithm can perform well with high accuracy. Experimental results demonstrate that our algorithm can achieve a larger detection range and exhibit lower time complexity in comparison with a previously proposed algorithm. [ABSTRACT FROM AUTHOR]

How to collect the real-time information of unconnected vehicles has been a challenge for connected vehicle technologies. The LiDAR sensors deployed along the roadside and at intersections provide a solution to fill the data gap during the transition from the traditional traffic to the full connected traffic. The roadside LiDAR sensors can record the movement of all road users with a relative long detection range. The lane detection serves as a fundamental but important step for LiDAR data processing. The location (which lane is occupied) of vehicles can be used for lane changing detection, vehicle departure warning and wrong-way alerts. But currently, there is not an effective method to identify the boundary of lanes using roadside LiDAR sensors. This paper presents a systematic procedure for lane detection based on the trajectories of vehicles collected on the road with the roadside LiDAR sensor. The whole procedure includes two major parts: background filtering and road boundary identification. This robust procedure can release engineers from the manual lane identification task and can avoid any error caused by manual work. Two case studies confirmed the effectiveness of the proposed method. Compared to previous lane detection methods, this procedure is not affected by the existence of pedestrians. This method can also detect the boundaries of lanes from the roads having curves with the limited time cost. [ABSTRACT FROM AUTHOR]

The existing connected-vehicle deployments obtain the real-time status of connected vehicles, but without knowing the unconnected traffic. It is urgent to find an approach to collecting the high-resolution real-time status of unconnected road users. This paper introduces a new-generation light detection and ranging (LiDAR) enhanced connected infrastructures that actively sense the high-resolution status of surrounding traffic participants with roadside LiDAR sensors and broadcast connected-vehicle messages through DSRC roadside units. The LiDAR data processing procedure, including background filtering, object clustering, vehicle recognition, lane identification, and vehicle tracking, is presented in this paper. The performance of the proposed data processing procedure is evaluated with the field collected data. [ABSTRACT FROM AUTHOR]

The BMW Group Research and Technology has been testing automated vehicles on Germany's highways since Spring 2011. Since then, thousands of kilometers have been driven on the highways around Munich, Germany. Throughout this project, fundamental technologies, such as environment perception, localization, driving strategy and vehicle control, were developed in order to safely operate prototype automated vehicles in real traffic with speeds up to 130 km/h. The goal of this project was to learn what technologies are necessary for automated driving. This paper presents the architecture and algorithms developed during this project, results from real driving scenarios, the lessons learned throughout the project and a quick introduction into the latest developments for improving the system. [ABSTRACT FROM PUBLISHER]