Rivero et al., 2021 - Google Patents
The effect of spray water on an automotive LIDAR sensor: A real-time simulation studyRivero et al., 2021
- Document ID
- 5501705116233863528
- Author
- Rivero J
- Gerbich T
- Buschardt B
- Chen J
- Publication year
- Publication venue
- IEEE Transactions on Intelligent Vehicles
External Links
Snippet
This paper presents the first of its kind real-time simulation of the effect of spray water on an automotive LIDAR sensor. The simulation is based on physically measurable quantities in order to facilitate its validation and extension. Both the sensor and the environment are …
- 239000007921 spray 0 title abstract description 101
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N15/00—Investigating characteristics of particles; Investigating permeability, pore-volume, or surface-area of porous materials
- G01N15/02—Investigating particle size or size distribution
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
- G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
- G01S17/88—Lidar systems specially adapted for specific applications
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Rivero et al. | The effect of spray water on an automotive LIDAR sensor: A real-time simulation study | |
| Dreissig et al. | Survey on lidar perception in adverse weather conditions | |
| Von Bernuth et al. | Simulating photo-realistic snow and fog on existing images for enhanced CNN training and evaluation | |
| US12505267B2 (en) | High fidelity simulations for autonomous vehicles based on retro-reflection metrology | |
| Chaabani et al. | A neural network approach to visibility range estimation under foggy weather conditions | |
| Teufel et al. | Simulating realistic rain, snow, and fog variations for comprehensive performance characterization of lidar perception | |
| Reway et al. | Test method for measuring the simulation-to-reality gap of camera-based object detection algorithms for autonomous driving | |
| US12153437B2 (en) | Detection of particulate matter in autonomous vehicle applications | |
| US12181584B2 (en) | Systems and methods for monitoring LiDAR sensor health | |
| Wang et al. | Simulation and application of cooperative driving sense systems using prescan software | |
| Yang et al. | Realistic rainy weather simulation for lidars in carla simulator | |
| Shih et al. | Reconstruction and synthesis of lidar point clouds of spray | |
| Yang et al. | Learn to model and filter point cloud noise for a near-infrared ToF LiDAR in adverse weather | |
| Pao et al. | Wind-driven rain effects on automotive camera and LiDAR performances | |
| Pao et al. | Wind tunnel testing methodology for autonomous vehicle optical sensors in adverse weather conditions | |
| Pan et al. | A novel computer vision‐based monitoring methodology for vehicle‐induced aerodynamic load on noise barrier | |
| Zhu et al. | Synthetic image generation model for intelligent vehicle camera function testing in rain and fog | |
| Wang et al. | Off-road testing scenario design and library generation for intelligent vehicles | |
| Scheuble et al. | Simulating road spray effects in automotive lidar sensor models | |
| Kassem et al. | Driver-in-the-Loop for computer-vision based ADAS testing | |
| Biedermann et al. | Congrats: Realistic simulation of traffic sequences for autonomous driving | |
| Qiu et al. | Using downward-looking lidar to detect and track traffic | |
| US20250384667A1 (en) | Machine learning system and method | |
| Xing et al. | Predictions of short-term driving intention using recurrent neural network on sequential data | |
| Chen | ADDRESSING THE INEFFICIENCIES IN CARLA SIMULATOR |