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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).

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.

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.

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 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.

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.

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.

We propose a lane marking detection algorithm based on solid-state LiDARs. First a series of data pre-processing operations were done for the solid-state LiDARs with small field of view, and the needed ground points are extracted by the RANSAC method. ...Vision based lane marking detection technology has been well discussed and put into practical application. LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. ...LiDAR is more stable for challenging environment compared to cameras, and with the development of LiDAR technology, price and lifetime are no longer an issue. We propose a lane marking detection algorithm based on solid-state LiDARs.

Camera and LiDAR are widely equipped on autonomous self-driving cars and developed with many algorithms in recent years. ...The fusion system of camera and LiDAR provides state-of the-art methods for environmental perception due to the defects of single vehicular sensor. ...In this paper, we assemble a test platform which is made up of dual LiDARs and one monocular camera and use the same sensing hardware architecture as intelligent sweeper designed by our laboratory.

Light detection and ranging (LiDAR) is an emerging technology in image segmentation that is able to estimate distances to the objects it detects. ...One advantage of LiDAR is the ability to gather accurate distances regardless of day, night, shadows, or glare. This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles. ...This study examines LiDAR and camera image segmentation fusion to improve an advanced driver-assistance systems (ADAS) algorithm for off-road autonomous military vehicles.

This method utilizes physics-based simulation of sensors, along with automated truth labeling, to improve the speed and accuracy of training data acquisition for both camera and LIDAR sensors. This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors. ...This framework is enabled by the MSU Autonomous Vehicle Simulator (MAVS), a physics-based sensor simulator for ground vehicle robotics that includes high-fidelity simulations of LIDAR, cameras, and other sensors.

Editorial AV lidar enters a new reality The Navigator UNECE takes first step toward AV regulation Special Delivery - by 'Bot Michigan start-up Refraction AI aims to be the go-to for last-mile, semi-autonomous, all-weather delivery robots. Lidar Infiltrates ADAS Once considered a necessity only in fully autonomous vehicles, lidar sensors are now being evaluated for lower-level driver-assistance systems. ...Editorial AV lidar enters a new reality The Navigator UNECE takes first step toward AV regulation Special Delivery - by 'Bot Michigan start-up Refraction AI aims to be the go-to for last-mile, semi-autonomous, all-weather delivery robots.

To overcome the limitations of physical testing, a physics-based simulation workflow was developed by coupling computational fluid dynamics (CFD) with optical simulations of camera and lidar sensors. The computational data of various weather conditions can be rapidly generated by CFD and used to assess the impact of weather conditions on the sensors and perception algorithms. ...The developed CFD-optical workflow was tested using rainy conditions as a test case, the data for which were generated using CFD and exported to optical simulation software to assess how rainy conditions affect the performance of a visible camera, lidar, and an open-source perception algorithm. The results of the analysis indicate that the rainy conditions and intensification of the conditions by other vehicles on the road degrade the performance of camera and lidar sensors. ...The results of the analysis indicate that the rainy conditions and intensification of the conditions by other vehicles on the road degrade the performance of camera and lidar sensors. The ability of the perception algorithm to detect vehicles significantly deteriorated when rainy conditions changed to moderate rain.