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Found 15 papers, 7 papers with code
The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data
In this article, we present a dataset with detailed manual annotations for different kinds of ghost detections.
Simulating Road Spray Effects in Automotive Lidar Sensor Models
The model conforms to the Open Simulation Interface (OSI) standard and is based on the formation of detection clusters within a spray plume.
Gated2Gated: Self-Supervised Depth Estimation from Gated Images
Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain.
A Benchmark for Spray from Nearby Cutting Vehicles
Current driver assistance systems and autonomous driving stacks are limited to well-defined environment conditions and geo fenced areas.
ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media
Most of today's supervised imaging and vision approaches, however, rely on training data collected in the real world that is biased towards good weather conditions, with dense fog, snow, and heavy rain as outliers in these datasets.
Using Machine Learning to Detect Ghost Images in Automotive Radar
We show that we can use a state-of-the-art automotive radar classifier in order to detect ghost objects alongside real objects.
Uncertainty depth estimation with gated images for 3D reconstruction
Gated imaging is an emerging sensor technology for self-driving cars that provides high-contrast images even under adverse weather influence.
Seeing Around Street Corners: Non-Line-of-Sight Detection and Tracking In-the-Wild Using Doppler Radar
In this work, we depart from visible-wavelength approaches and demonstrate detection, classification, and tracking of hidden objects in large-scale dynamic environments using Doppler radars that can be manufactured at low-cost in series production.
A Benchmark for Lidar Sensors in Fog: Is Detection Breaking Down?
Autonomous driving at level five does not only means self-driving in the sunshine.
Benchmarking Image Sensors Under Adverse Weather Conditions for Autonomous Driving
Adverse weather conditions are very challenging for autonomous driving because most of the state-of-the-art sensors stop working reliably under these conditions.
Learning Super-resolved Depth from Active Gated Imaging
Environment perception for autonomous driving is doomed by the trade-off between range-accuracy and resolution: current sensors that deliver very precise depth information are usually restricted to low resolution because of technology or cost limitations.
Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios
This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available.
Weather Influence and Classification with Automotive Lidar Sensors
Lidar sensors are often used in mobile robots and autonomous vehicles to complement camera, radar and ultrasonic sensors for environment perception.
Seeing Through Fog Without Seeing Fog: Deep Multimodal Sensor Fusion in Unseen Adverse Weather
The fusion of multimodal sensor streams, such as camera, lidar, and radar measurements, plays a critical role in object detection for autonomous vehicles, which base their decision making on these inputs.
Ranked #2 on
2D Object Detection
on Clear Weather
Gated2Depth: Real-time Dense Lidar from Gated Images
The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges.
