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The critical points on the rail and road network are their intersections, i.e., level crossings. During a train crossing, car traffic is stopped. This reduces the fluidity of traffic on the road and, consequently, can cause congestion. The problem increases with the number of cars and trains. Frequently, due to national regulations, level crossing closure times are long. It is mainly dictated by safety issues. Building two-level intersections is not always a good solution, mainly because of the high cost of implementation. In the article, the authors proposed the use of sensors to reduce level crossing closure times and improve the Level of Service on the road network. The analyzed railroad lines are local agglomeration lines, mainly due to safety (low speed of commuter trains) and high impact on the road network. The sensors proposed in the article are based on radar/LIDAR. Formulas similar to HCM methods are proposed, which can be implemented in a railroad crossing controller. Simulations using the PTV Vissim program are carried out and the results are worked out based on the obtained data. The considered method can reduce the level crossing closure time by 68.6%, thereby increasing the Level of Service on roads near railroads. [ABSTRACT FROM AUTHOR]

With the increasing deployment of autonomous taxis in different cities around the world, recent studies have stressed the importance of developing new methods, models and tools for intuitive human–autonomous taxis interactions (HATIs). Street hailing is one example, where passengers would hail an autonomous taxi by simply waving a hand, exactly like they do for manned taxis. However, automated taxi street-hailing recognition has been explored to a very limited extent. In order to address this gap, in this paper, we propose a new method for the detection of taxi street hailing based on computer vision techniques. Our method is inspired by a quantitative study that we conducted with 50 experienced taxi drivers in the city of Tunis (Tunisia) in order to understand how they recognize street-hailing cases. Based on the interviews with taxi drivers, we distinguish between explicit and implicit street-hailing cases. Given a traffic scene, explicit street hailing is detected using three elements of visual information: the hailing gesture, the person's relative position to the road and the person's head orientation. Any person who is standing close to the road, looking towards the taxi and making a hailing gesture is automatically recognized as a taxi-hailing passenger. If some elements of the visual information are not detected, we use contextual information (such as space, time and weather) in order to evaluate the existence of implicit street-hailing cases. For example, a person who is standing on the roadside in the heat, looking towards the taxi but not waving his hand is still considered a potential passenger. Hence, the new method that we propose integrates both visual and contextual information in a computer-vision pipeline that we designed to detect taxi street-hailing cases from video streams collected by capturing devices mounted on moving taxis. We tested our pipeline using a dataset that we collected with a taxi on the roads of Tunis. Considering both explicit and implicit hailing scenarios, our method yields satisfactory results in relatively realistic settings, with an accuracy of 80%, a precision of 84% and a recall of 84%. [ABSTRACT FROM AUTHOR]

In recent years, intelligent driving technology based on vehicle–road cooperation has gradually become a research hotspot in the field of intelligent transportation. There are many studies regarding vehicle perception, but fewer studies regarding roadside perception. As sensors are installed at different heights, the roadside object scale varies violently, which burdens the optimization of networks. Moreover, there is a large amount of overlapping and occlusion in complex road environments, which leads to a great challenge of object distinction. To solve the two problems raised above, we propose RD-YOLO. Based on YOLOv5s, we reconstructed the feature fusion layer to increase effective feature extraction and improve the detection capability of small targets. Then, we replaced the original pyramid network with a generalized feature pyramid network (GFPN) to improve the adaptability of the network to different scale features. We also integrated a coordinate attention (CA) mechanism to find attention regions in scenarios with dense objects. Finally, we replaced the original Loss with Focal-EIOU Loss to improve the speed of the bounding box regression and the positioning accuracy of the anchor box. Compared to the YOLOv5s, the RD-YOLO improves the mean average precision (mAP) by 5.5% on the Rope3D dataset and 2.9% on the UA-DETRAC dataset. Meanwhile, by modifying the feature fusion layer, the weight of RD-YOLO is decreased by 55.9% while the detection speed is almost unchanged. Nevertheless, the proposed algorithm is capable of real-time detection at faster than 71.9 frames/s (FPS) and achieves higher accuracy than the previous approaches with a similar FPS. [ABSTRACT FROM AUTHOR]

The United States has over three trillion vehicle miles of travel annually on over four million miles of public roadways, which require regular maintenance. To maintain and improve these facilities, agencies often temporarily close lanes, reconfigure lane geometry, or completely close the road depending on the scope of the construction project. Lane widths of less than 11 feet in construction zones can impact highway capacity and crash rates. Crash data can be used to identify locations where the road geometry could be improved. However, this is a manual process that does not scale well. This paper describes findings for using data from onboard sensors in production vehicles for measuring lane widths. Over 200 miles of roadway on US-52, US-41, and I-65 in Indiana were measured using vehicle sensor data and compared with mobile LiDAR point clouds as ground truth and had a root mean square error of approximately 0.24 feet. The novelty of these results is that vehicle sensors can identify when work zones use lane widths substantially narrower than the 11 foot standard at a network level and can be used to aid in the inspection and verification of construction specification conformity. This information would contribute to the construction inspection performed by agencies in a safer, more efficient way. [ABSTRACT FROM AUTHOR]

Several industrial pick-and-place applications, such as collaborative assembly lines, rely on visual tracking of the parts. Recurrent occlusions are caused by the manipulator motion decrease line productivity and can provoke failures. This work provides a complete solution for maintaining the occlusion-free line of sight between a variable-pose camera and the object to be picked by a 6R manipulator that is not wrist-partitioned. We consider potential occlusions by the manipulator as well as the operator working at the assembly station. An actuated camera detects the object goal (part to pick) and keeps track of the operator. The approach consists of using the complete set of solutions obtained from the derivation of the univariate polynomial equation solution to the inverse kinematics (IK). Compared to numerical iterative solving methods, our strategy grants us a set of joint positions (posture) for each root of the equation from which we extract the best (minimizing the risks of occlusion). Our analytical-based method, integrating collision and occlusion avoidance optimizations, can contribute to greatly enhancing the efficiency and safety of collaborative assembly workstations. We validate our approach with simulations as well as with physical deployments on commercial hardware. [ABSTRACT FROM AUTHOR]

Unmanned aerial vehicles (UAV) are envisioned to become one of the new types of fifth/sixth generation (5G/6G) network users. To support advanced services for UAVs such as video monitoring, one of the prospective options is to utilize recently standardized New Radio (NR) technology operating in the millimeter-wave (mmWave) frequency band. However, blockage of propagation paths between NR base stations (BS) and UAV by buildings may lead to frequent outage situations. In our study, we use the tools of integral geometry to characterize connectivity properties of UAVs in terrestrial urban deployments of mmWave NR systems using UAV line-of-sight (LoS) blockage probability as the main metric of interest. As opposed to other studies, the use of the proposed approach allows us to get closed-form approximation for LoS blockage probability as a function of city and network deployment parameters. As one of the options to improve connectivity we also consider rooftop-mounted mmWave BSs. Our results illustrate that the proposed model provides an upper bound on UAV LoS blockage probability, and this bound becomes more accurate as the density of mmWave BS in the area increases. The closed-form structure allows for identifying of the street width, building block and BS heights, and UAV altitude as the parameters providing the most impact on the considered metric. We show that rooftop-mounted mmWave BSs allow for the drastic improvement of LoS blockage probability, i.e., depending on the system parameters the use of one rooftop-mounted mmWave BS is equivalent to 6–12 ground-mounted mmWave BSs. Out of all considered deployment parameters the street width is the one most heavily affecting the UAV LoS blockage probability. Specifically, the deployment with street width of 20 m is characterized by 50% lower UAV LoS blockage probability as compared to the one with 10 m street width. [ABSTRACT FROM AUTHOR]

Lane mark detection plays an important role in autonomous driving under structural environments. Many deep learning-based lane mark detection methods have been put forward in recent years. However, most of current methods limit their solutions within one single image and do not make use of the de facto successive image input during the driving scene, which may lead to inferior performance in some challenging scenarios such as occlusion, shadows, and lane mark degradation. To address the issue, we propose a novel lane mark detection network which takes pre-aligned multiple successive frames as inputs to produce more stable predictions. A Spatial-Temporal Attention Module (STAM) is designed in the network to adaptively aggregate the feature information of history frames to the current frame. Various structure of the STAM is also studied to ensure the best performance. Experiments on Tusimple and ApolloScape datasets show that our method can effectively improve lane mark detection and achieve state-of-the-art performance. [ABSTRACT FROM AUTHOR]