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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.

Recent developments in the area of autonomous vehicle navigation have emphasized algorithm development for the characterization of LiDAR 3D point-cloud data. The LiDAR sensor data provides a detailed understanding of the environment surrounding the vehicle for safe navigation. ...However, LiDAR point cloud datasets need point-level labels which require a significant amount of annotation effort. ...The simulated LiDAR data was generated by a physics-based platform, the Mississippi State University Autonomous Vehicle Simulator (MAVS).

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.

This paper explores how to enhance your autonomous system (AS) testing capabilities and quality assurance using a completely automated hardware-in-the-loop (HIL) test environment that interfaces to or simulates autonomous sensor technology, such as cameras, radar, LIDAR, and other key technologies, such as GNSS/maps and V2X communication. The key to performing such real-time testing is the ability to stimulate the various electronic control units (ECUs)/sensors through closed-loop simulation of the vehicle, its environment, traffic, surroundings, etc., along with playback of captured sensor data and its synchronization with key vehicle bus and application data.

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.

The proposed system employs Light Detection and Ranging (LIDAR) and passive electro-optics equipment installed in two non-collocated components. The source component consists of a tuneable small-size and low-cost/weight LIDAR emitter, which can be installed either on airborne or ground-based autonomous vehicles, or in fixed surface installations. ...The source component consists of a tuneable small-size and low-cost/weight LIDAR emitter, which can be installed either on airborne or ground-based autonomous vehicles, or in fixed surface installations. ...The proposed bistatic system determines the column-averaged molecular and aerosol pollutant concentrations along the LIDAR beam by measuring the cumulative absorption and scattering phenomena along the optical slant range.

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.

The bi-directional bonus for EVs Bi-directional charging is a value-added feature that seems certain to help accelerate EV adoption. Lidar tech illuminates CES 2022 Lidar suppliers tout increased perception, smaller form factors and mass-production capabilities as the sensors begin moving to mainstream applications. ...Editorial Optimizing EV platforms for pickup trucks Supplier Eye Time to turn 'BEV' into a verb Valeo debuts new lidars, EV tech at CES 2022 Hyundai enters the metaverse via its PnD technology GM unveils 2024 Chevrolet Silverado EV Barra promises $30K EV SUV by fall 2023; automated driving by mid-decade Panasonic augments driver safety with new HUD tech Unique Helmholtz induction for new GM LT6 V8 Q&A Uwe Gackstatter, president of the Powertrain Solutions Division at Robert Bosch, on why markets will decide propulsion-tech mix - and ICE development can't cease.

The described thermal model is part of the Lidar Performance Analysis Simulator (LIPAS), a simulator of the Atmospheric Laser Doppler Instrument ALADIN.

This paper describes the methods used to collect and reduce wake turbulence data behind two distinct types of tiltrotor aircraft using a Light Detection and Ranging (LIDAR) measurement system, which uses laser velocimetry to measure the velocity of dust particles in air that has been disturbed by the passage of an aircraft. ...The MIT LL LIDAR system measured the wake vortices with minimal pre-test preparation; we obtained a large quantity of high quality data in only a few days of testing.

Expanding ADAS roles for radar and cameras Evolving into lidar alternatives, the bread-and-butter sensors of ADAS are seeing potential far beyond commodity status.

Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving. Also, even after decades of research, the reasonable place for ‘mid’ in the End-to-End approach, as well as, the trade-off between data-driven and math-based approach is not fully understood.

Digital 3-D models of a test track facility may be available from the construction of the grounds, or they can be created with laser measurement techniques, such as LIDAR. Tire-terrain contact patch simulation techniques provide for the use of validated physics models to study 3-D vehicle behavior undergoing simulated tests for handling, ride, braking compliance or other maneuvers.

Our work merges the traffic simulation software Vissim to create realistic traffic, the vehicle dynamic simulation software CarMaker along with soft-sensors such as 3D Lidar and Camera, and the dedicated Nvidia Drive PX2 hardware platform for autonomous vehicles for data processing and decision-making in order to bring together simulation environments into a single simulation platform.

The Rodney Dangerfield of Automated-Driving Sensors Radar and lidar get all the attention, but Inertial Measurement Units are the backbone of sensor fusion. Suppliers are scrambling to make IMUs more accurate-and much less expensive.

A cooperation between many sensors such as LIDAR, Radar and camera helps to enhance the safety. Apart from the importance of having a reliable object detector, the safety system should have requisite capabilities to make reasonable decisions in the moment.

A laboratory experimental setup is being commissioned where three different types of sensors, a digital camera with 8 megapixel resolution, a LIDAR with 40m range, and ultrasonic distance transducer sensors will be used for multi-sensor fusion to identify the object in real-time.

The Chrono::Sensor module provides simulation of a variety of sensor (IMU, GPS, Camera, Lidar); the exteroceptive sensors simulation is based on ray-tracing. SynChrono was demonstrated to run more than 120 vehicles in real time, using the Message Passing Interface (MPI) standard in which one CPU core runs the simulation of one vehicle.