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This paper presents a proof of concept for an algorithm to determine the attitude of a multi-layer laser rangefinder or LiDAR (Light Detection And Ranging) relative to a reference frame given the ability of the LiDAR to make measurements to a planar surface with sufficient and proper excitation.

This paper aims to elucidate the development and validation of a phenomenological LIDAR sensor model, as well as its utilization in the development of sensor fusion algorithms. By leveraging this approach, researchers can effectively simulate sensor behavior, facilitate faster development cycles, and enhance algorithmic advancements in autonomous systems.

A Light Detection And Ranging (LiDAR) is now becoming an essential sensor for an autonomous vehicle. The LiDAR provides the surrounding environment information of the vehicle in the form of a point cloud. ...A decision-making system of the autonomous car is able to determine a safe and comfort maneuver by utilizing the detected LiDAR point cloud. The LiDAR points on the cloud are classified as dynamic or static class depending on the movement of the object being detected. ...If the movement class (dynamic or static) of detected points can be provided by LiDAR, the decision-making system is able to plan the appropriate motion of the autonomous vehicle according to the movement of the object.

Building on earlier work from the IAM in creating an infrastructure- based sensor system to evaluate OSA metrics in real- world scenarios, this paper presents an approach for real-time localization and velocity estimation for AVs using a network of LiDAR sensors. The LiDAR data are captured by a network of three Luminar LiDAR sensors at an intersection in Anthem, AZ, while camera data are collected from the same intersection. ...The accuracy of both the localization and velocity estimation using LiDAR is assessed by comparing the LiDAR estimated state vectors against the differential GPS position and velocity measurements from a test vehicle passing through the intersection, as well as against a camera-based algorithm applied on drone video footage It is shown that the proposed method, taking advantage of simultaneous data capture from multiple LiDAR sensors, offers great potential for fast, accurate operational safety assessment of AV’s with an average localization error of only 10 cm observed between LiDAR and real-time differential GPS position data, when tracking a vehicle over 170 meters of roadway. ...Additional sensor modalities such as Light Detecting and Ranging (LiDAR) sensors allow for a wider range of scenarios to be accommodated and may also provide improved measurements of the Operational Safety Assessment (OSA) metrics previously introduced by the Institute of Automated Mobility (IAM).

LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. ...LiDAR sensors have played more and more important role on Intelligent and Connected Vehicles (ICV) and Advanced Driver Assistance Systems (ADAS) .However, the development and testing of LiDAR sensors under real driving environment for ADAS applications are greatly limited by various factors, and often are impossible due to safety concerns. ...This paper proposed a novel functional LiDAR model under virtual driving environment to support development of LiDAR-based ADAS applications under early stage.

To be able to transfer highly automated driving functions into a simulation, models of the vehicle’s perception sensors such as lidar, radar and camera are required. In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. ...In addition to the classic pulsed time-of-flight (ToF) lidars, the growing availability of commercial frequency modulated continuous wave (FMCW) lidars sparks interest in the field of environment perception. This is due to advanced capabilities such as directly measuring the target’s relative radial velocity based on the Doppler effect. ...In this work, an FMCW lidar sensor simulation model is introduced, which is divided into the components of signal propagation and signal processing.

This paper describes a novel recognition and classification method of vehicle targets in urban road based on a vehicle-mounted Velodyne HDL64E light detection and ranging (LIDAR) system. The autonomous vehicle will choose different driving strategy according to the surrounding traffic environments to guarantee that the driving is safe, stable and efficient. ...Range-imagine can be achieved by projecting the 3D points to a 2.5D grid and taking the LIDAR (Light Detection and Ranging) origin point as the project origin. In this method, the transform just uses in the each cluster instead of whole 3D points.

Light detection and ranging (LiDAR) has become an essential technique for HD map construction, environmental perception, localization, and other ICV tasks due to its advantage of high scanning accuracy and dense point cloud generation.

The accident reconstruction community relies on photogrammetry for taking measurements from photographs. Camera matching, a close-range photogrammetry method, is a particularly useful tool for locating accident scene evidence after time has passed and the evidence is no longer physically visible. In this method, objects within the accident scene that have remained unchanged are used as a reference for locating evidence that is no longer physically available at the scene such as tire marks, gouge marks, and vehicle points of rest. Roadway lines, edges of pavement, sidewalks, signs, posts, buildings, and other structures are recognizable scene features that if unchanged between the time of accident and time of analysis are beneficial to the photogrammetric process. In instances where these scene features are limited or do not exist, achieving accurate photogrammetric solutions can be challenging.

Position estimation of the surrounding objects seen by the sensors mounted on an autonomous vehicle is a key module and it is typically carried out with the camera-lidar fusion owing to the high accuracy in depth estimation from lidar point cloud. In typical automotive LIDAR with 64 scanner points or less, at distances above 100 m, the object detection with LIDAR is not dependable as the number of LIDAR clusters will be sparse, while the high-resolution camera can offer better detection even at the distances above 100 m. ...In typical automotive LIDAR with 64 scanner points or less, at distances above 100 m, the object detection with LIDAR is not dependable as the number of LIDAR clusters will be sparse, while the high-resolution camera can offer better detection even at the distances above 100 m. ...Our proposed approach is a novel grid-based approach based on fusion of camera object detection and panoptic segmentation, which is then associated with lidar point cloud data and lidar object detections for accurate distance estimation. We take into consideration the occlusion level of the camera detected objects with the help of panoptic segmentation of the image frames and only the lidar points corresponding to the actual visible points of the object is used further for fusion and distance estimation.

This challenge is addressed in this work for lidar sensors. The lidar equation was previously employed in this context to derive estimates of a lidar’s maximum range in different weather conditions. ...In this work, we present a stochastic simulation framework based on a probabilistic extension of the lidar equation, to quantify the effect of adverse rainfall conditions on a lidar’s raw detection performance. ...Combining the effect of other noise sources with the presented framework in a ray tracer is an opportunity for realistic physical lidar simulations and would allow to virtually estimate the performance of a lidar’s object detection and tracking performance.

LiDAR sensors are common in automated driving due to their high accuracy. However, LiDAR processing algorithm development suffers from lack of diverse training data, partly due to sensors’ high cost and rapid development cycles. ...We address the unmet need for abundant, high-quality LiDAR data with the development of a synthetic LiDAR point cloud generation tool and validate this tool’s performance using the KITTI-trained PIXOR object detection model. ...This approach will support low-cost bulk generation of accurate data for training advanced selfdriving algorithms, with configurability to simulate existing and upcoming LiDAR configurations possessing varied channels, range, vertical and horizontal fields of view, and angular resolution.

In 2017 the United States Geological Survey (USGS) released historical 3D point clouds (LiDAR) allowing for access to digital 3D data without visiting the site. This offers many unique benefits to the reconstruction community including: safety, budget, time, and historical preservation. ...To determine accuracies achievable using this method, evidence locations solved for using only USGS LiDAR, aerial images and scene photographs (representative of emergency personnel photographs) were compared with known locations documented using total station survey equipment and ground-based 3D laser scanning. ...To further evaluate the quality of the USGS LiDAR, a comparative point cloud analysis of the roadway surfaces was performed. On average, 85% of the USGS LiDAR points were found to be within .5 inches of the ground-based 3D scanning points.

In this paper, the signal data processing of sensed LIDAR and the multiple target track management algorithm of a maneuvering vehicle are presented. ...The algorithm is employed for 2D LIDAR sensor mounted in a moving vehicle and navigating in a high-way. The adaptive segmentation, feature creation from point cloud, data association and prediction modelling are the key features of track management.

Further visor design exploration and optimization was performed to find designs that do not block the lidar field of view (FOV) while optimizing lidar cleaning performance. One hundred designs were explored using the STAR-CCM+ design exploration tool, and the best designs showed that they can completely deflect incoming headwind at high vehicle speeds while not obstructing the FOV. ...The performance of autonomous vehicle (AV) sensors, such as lidars or cameras, is often hindered during rain. Rain droplets on the AV sensors can cause beam attenuation and backscattering, which in turn causes inaccurate sensor readings and misjudgment by AV algorithms.

The main research content of this paper is to design a positioning algorithm for unmanned terminal Automated Guided Vehicle (AGV) based on single-line lidar, including point cloud data acquisition, background filtering, point cloud clustering, vehicle position extraction, and result optimization.

The sensors used in this study were one camera and one LiDAR. These sensors were connected to an NVIDIA Drive Px2 which operated in a 2019 Kia Niro. Two custom scenarios (static and dynamic objects) were chosen to collect sensor data operating in four real-world weather conditions: fair, cloudy, rainy, and light snow.

This approach uses measurement from an array of Lidars and video frames from a camera and derives traffic information using two techniques. The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. ...The first technique detects passing vehicles by using Lidars to constantly measure the distance from laser transmitter to the target road surface. When a vehicle or other objects pass by, the measurement of the distance to road surface reduces in each targeting spot, and triggers detection event.

This paper describes the analysis and performance testing of a uniquely designed external heat exchanger. The heat exchanger is attached externally to an aircraft and is used to cool a laser system within the fuselage. Estimates showed insufficient cooling capacity with a conventional staggered tube array in the limited space available. Thus, a non-conventional design was developed with larger tube and fin area exposed to the ram air to increase the heat transfer performance. The basic design consists of 28 circular finned aluminum tubes arranged in two parallel banks. Wind tunnel tests were performed to simulate air and liquid flight conditions for the non-conventional parallel bank arrangement and the conventional staggered tube arrangement. Performance comparisons of each of the two designs are presented. Test results are used in a computer model of the heat exchanger to predict the operating performance for the entire flight profile.

A Kalman Filter is used to fuse GPS, IMU and LiDAR data for vehicle localization in the CARLA simulator. This vehicle localization algorithm was then evaluated using resilience engineering metrics in the simulated multipath and overpass scenario.