Can you see me now? Blind spot estimation for autonomous vehicles using
scenario-based simulation with random reference sensors
- URL: http://arxiv.org/abs/2402.00467v2
- Date: Wed, 14 Feb 2024 15:19:02 GMT
- Title: Can you see me now? Blind spot estimation for autonomous vehicles using
scenario-based simulation with random reference sensors
- Authors: Marc Uecker and J.Marius Z\"ollner
- Abstract summary: A Monte Carlo-based reference sensor simulation enables us to accurately estimate blind spot size as a metric of coverage.
Our method leverages point clouds from LiDAR sensors or camera depth images from high-fidelity simulations of target scenarios to provide accurate and actionable visibility estimates.
- Score: 5.910402196056647
- License: http://creativecommons.org/licenses/by-sa/4.0/
- Abstract: In this paper, we introduce a method for estimating blind spots for sensor
setups of autonomous or automated vehicles and/or robotics applications. In
comparison to previous methods that rely on geometric approximations, our
presented approach provides more realistic coverage estimates by utilizing
accurate and detailed 3D simulation environments. Our method leverages point
clouds from LiDAR sensors or camera depth images from high-fidelity simulations
of target scenarios to provide accurate and actionable visibility estimates. A
Monte Carlo-based reference sensor simulation enables us to accurately estimate
blind spot size as a metric of coverage, as well as detection probabilities of
objects at arbitrary positions.
Related papers
- OccNeRF: Advancing 3D Occupancy Prediction in LiDAR-Free Environments [77.0399450848749]
We propose an OccNeRF method for training occupancy networks without 3D supervision.
We parameterize the reconstructed occupancy fields and reorganize the sampling strategy to align with the cameras' infinite perceptive range.
For semantic occupancy prediction, we design several strategies to polish the prompts and filter the outputs of a pretrained open-vocabulary 2D segmentation model.
arXiv Detail & Related papers (2023-12-14T18:58:52Z) - 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) - Mapping LiDAR and Camera Measurements in a Dual Top-View Grid
Representation Tailored for Automated Vehicles [3.337790639927531]
We present a generic evidential grid mapping pipeline designed for imaging sensors such as LiDARs and cameras.
Our grid-based evidential model contains semantic estimates for cell occupancy and ground separately.
Our method estimates cell occupancy robustly and with a high level of detail while maximizing efficiency and minimizing the dependency to external processing modules.
arXiv Detail & Related papers (2022-04-16T23:51:20Z) - SurroundDepth: Entangling Surrounding Views for Self-Supervised
Multi-Camera Depth Estimation [101.55622133406446]
We propose a SurroundDepth method to incorporate the information from multiple surrounding views to predict depth maps across cameras.
Specifically, we employ a joint network to process all the surrounding views and propose a cross-view transformer to effectively fuse the information from multiple views.
In experiments, our method achieves the state-of-the-art performance on the challenging multi-camera depth estimation datasets.
arXiv Detail & Related papers (2022-04-07T17:58:47Z) - iSDF: Real-Time Neural Signed Distance Fields for Robot Perception [64.80458128766254]
iSDF is a continuous learning system for real-time signed distance field reconstruction.
It produces more accurate reconstructions and better approximations of collision costs and gradients.
arXiv Detail & Related papers (2022-04-05T15:48:39Z) - 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) - A Multi-Layered Approach for Measuring the Simulation-to-Reality Gap of
Radar Perception for Autonomous Driving [0.0]
In order to rely on virtual tests the employed sensor models have to be validated.
There exists no sound method to measure this simulation-to-reality gap of radar perception.
We have shown the effectiveness of the proposed approach in terms of providing an in-depth sensor model assessment.
arXiv Detail & Related papers (2021-06-15T18:51:39Z) - Baseline and Triangulation Geometry in a Standard Plenoptic Camera [6.719751155411075]
We present a geometrical light field model allowing triangulation to be applied to a plenoptic camera.
It is shown that distance estimates from our novel method match those of real objects placed in front of the camera.
arXiv Detail & Related papers (2020-10-09T15:31:14Z) - 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) - Ego-motion and Surrounding Vehicle State Estimation Using a Monocular
Camera [11.29865843123467]
We propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera.
Our approach is based on a combination of three deep neural networks to estimate the 3D vehicle bounding box, depth, and optical flow from a sequence of images.
arXiv Detail & Related papers (2020-05-04T16:41:38Z)
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.