Simulating Road Spray Effects in Automotive Lidar Sensor Models
- URL: http://arxiv.org/abs/2212.08558v1
- Date: Fri, 16 Dec 2022 16:25:36 GMT
- Title: Simulating Road Spray Effects in Automotive Lidar Sensor Models
- Authors: Clemens Linnhoff, Dominik Scheuble, Mario Bijelic, Lukas Elster,
Philipp Rosenberger, Werner Ritter, Dengxin Dai and Hermann Winner
- Abstract summary: In this work, a novel modeling approach for spray in lidar data is introduced.
The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume.
It is shown that the model helps to improve detection in real-world spray scenarios significantly.
- Score: 22.047932516111732
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Modeling perception sensors is key for simulation based testing of automated
driving functions. Beyond weather conditions themselves, sensors are also
subjected to object dependent environmental influences like tire spray caused
by vehicles moving on wet pavement. In this work, a novel modeling approach for
spray in lidar data is introduced. The model conforms to the Open Simulation
Interface (OSI) standard and is based on the formation of detection clusters
within a spray plume. The detections are rendered with a simple custom ray
casting algorithm without the need of a fluid dynamics simulation or physics
engine. The model is subsequently used to generate training data for object
detection algorithms. It is shown that the model helps to improve detection in
real-world spray scenarios significantly. Furthermore, a systematic real-world
data set is recorded and published for analysis, model calibration and
validation of spray effects in active perception sensors. Experiments are
conducted on a test track by driving over artificially watered pavement with
varying vehicle speeds, vehicle types and levels of pavement wetness. All
models and data of this work are available open source.
Related papers
- Sensitivity analysis of AI-based algorithms for autonomous driving on
optical wavefront aberrations induced by the windshield [4.542616945567623]
This paper investigates the domain shift problem by evaluating the sensitivity of two perception models to different windshield configurations.
Our results show that there is a performance gap introduced by windshields and existing optical metrics used for posing requirements might not be sufficient.
arXiv Detail & Related papers (2023-08-19T17:01:23Z) - On Transferability of Driver Observation Models from Simulated to Real
Environments in Autonomous Cars [23.514129229090987]
This paper investigates the viability of transferring video-based driver observation models from simulation to real-world scenarios in autonomous vehicles.
We record a dataset featuring actual autonomous driving conditions and involving seven participants engaged in highly distracting secondary activities.
Our dataset was designed in accordance with an existing large-scale simulator dataset used as the training source.
arXiv Detail & Related papers (2023-07-31T10:18:49Z) - Automated Automotive Radar Calibration With Intelligent Vehicles [73.15674960230625]
We present an approach for automated and geo-referenced calibration of automotive radar sensors.
Our method does not require external modifications of a vehicle and instead uses the location data obtained from automated vehicles.
Our evaluation on data from a real testing site shows that our method can correctly calibrate infrastructure sensors in an automated manner.
arXiv Detail & Related papers (2023-06-23T07:01:10Z) - TrafficBots: Towards World Models for Autonomous Driving Simulation and
Motion Prediction [149.5716746789134]
We show data-driven traffic simulation can be formulated as a world model.
We present TrafficBots, a multi-agent policy built upon motion prediction and end-to-end driving.
Experiments on the open motion dataset show TrafficBots can simulate realistic multi-agent behaviors.
arXiv Detail & Related papers (2023-03-07T18:28:41Z) - Learning to Simulate Realistic LiDARs [66.7519667383175]
We introduce a pipeline for data-driven simulation of a realistic LiDAR sensor.
We show that our model can learn to encode realistic effects such as dropped points on transparent surfaces.
We use our technique to learn models of two distinct LiDAR sensors and use them to improve simulated LiDAR data accordingly.
arXiv Detail & Related papers (2022-09-22T13:12:54Z) - DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly
detection in air transportation [68.8204255655161]
We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE)
It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase.
Results show that the DAE achieves better results in both accuracy and speed of detection.
arXiv Detail & Related papers (2021-09-08T14:07:55Z) - Lidar Light Scattering Augmentation (LISA): Physics-based Simulation of
Adverse Weather Conditions for 3D Object Detection [60.89616629421904]
Lidar-based object detectors are critical parts of the 3D perception pipeline in autonomous navigation systems such as self-driving cars.
They are sensitive to adverse weather conditions such as rain, snow and fog due to reduced signal-to-noise ratio (SNR) and signal-to-background ratio (SBR)
arXiv Detail & Related papers (2021-07-14T21:10:47Z) - Testing the Safety of Self-driving Vehicles by Simulating Perception and
Prediction [88.0416857308144]
We propose an alternative to sensor simulation, as sensor simulation is expensive and has large domain gaps.
We directly simulate the outputs of the self-driving vehicle's perception and prediction system, enabling realistic motion planning testing.
arXiv Detail & Related papers (2020-08-13T17:20:02Z) - A Sensitivity Analysis Approach for Evaluating a Radar Simulation for
Virtual Testing of Autonomous Driving Functions [0.0]
We introduce a sensitivity analysis approach for developing and evaluating a radar simulation.
A modular radar system simulation is presented and parameterized to conduct a sensitivity analysis.
We compare the output from the radar model to real driving measurements to ensure a realistic model behavior.
arXiv Detail & Related papers (2020-08-06T15:51:52Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.