Fast Rule-Based Clutter Detection in Automotive Radar Data

• URL: http://arxiv.org/abs/2108.12224v1
• Date: Fri, 27 Aug 2021 11:32:50 GMT
• Title: Fast Rule-Based Clutter Detection in Automotive Radar Data
• Authors: Johannes Kopp, Dominik Kellner, Aldi Piroli, Klaus Dietmayer
• Abstract summary: Automotive radar sensors output a lot of unwanted clutter or ghost detections. clutter detections occur in groups or at similar locations in multiple consecutive measurements. New algorithm for identifying such erroneous detections is presented.
• Score: 10.379073531824456
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automotive radar sensors output a lot of unwanted clutter or ghost detections, whose position and velocity do not correspond to any real object in the sensor's field of view. This poses a substantial challenge for environment perception methods like object detection or tracking. Especially problematic are clutter detections that occur in groups or at similar locations in multiple consecutive measurements. In this paper, a new algorithm for identifying such erroneous detections is presented. It is mainly based on the modeling of specific commonly occurring wave propagation paths that lead to clutter. In particular, the three effects explicitly covered are reflections at the underbody of a car or truck, signals traveling back and forth between the vehicle on which the sensor is mounted and another object, and multipath propagation via specular reflection. The latter often occurs near guardrails, concrete walls or similar reflective surfaces. Each of these effects is described both theoretically and regarding a method for identifying the corresponding clutter detections. Identification is done by analyzing detections generated from a single sensor measurement only. The final algorithm is evaluated on recordings of real extra-urban traffic. For labeling, a semi-automatic process is employed. The results are promising, both in terms of performance and regarding the very low execution time. Typically, a large part of clutter is found, while only a small ratio of detections corresponding to real objects are falsely classified by the algorithm.

Related papers

• Learning 3D Perception from Others' Predictions [64.09115694891679]
  We investigate a new scenario to construct 3D object detectors: learning from the predictions of a nearby unit that is equipped with an accurate detector. For example, when a self-driving car enters a new area, it may learn from other traffic participants whose detectors have been optimized for that area.
  arXiv  Detail & Related papers  (2024-10-03T16:31:28Z)
• Multi-Object Tracking based on Imaging Radar 3D Object Detection [0.13499500088995461]
  This paper presents an approach for tracking surrounding traffic participants with a classical tracking algorithm. Learning based object detectors have been shown to work adequately on lidar and camera data, while learning based object detectors using standard radar data input have proven to be inferior. With the improvements to radar sensor technology in the form of imaging radars, the object detection performance on radar was greatly improved but is still limited compared to lidar sensors due to the sparsity of the radar point cloud. The tracking algorithm must overcome the limited detection quality while generating consistent tracks.
  arXiv  Detail & Related papers  (2024-06-03T05:46:23Z)
• Fairness in Autonomous Driving: Towards Understanding Confounding Factors in Object Detection under Challenging Weather [7.736445799116692]
  This study provides an empirical analysis of fairness in detecting pedestrians in a state-of-the-art transformer-based object detector. In addition to classical metrics, we introduce novel probability-based metrics to measure various intricate properties of object detection. Our quantitative analysis reveals how the previously overlooked yet intuitive factors, such as the distribution of demographic groups in the scene, the severity of weather, the pedestrians' proximity to the AV, among others, affect object detection performance.
  arXiv  Detail & Related papers  (2024-05-31T22:35:10Z)
• The Radar Ghost Dataset -- An Evaluation of Ghost Objects in Automotive Radar Data [12.653873936535149]
  A lot more surfaces in a typical traffic scenario appear flat relative to the radar's emitted signal. This results in multi-path reflections or so called ghost detections in the radar signal. We present a dataset with detailed manual annotations for different kinds of ghost detections.
  arXiv  Detail & Related papers  (2024-04-01T19:20:32Z)
• Simultaneous Clutter Detection and Semantic Segmentation of Moving Objects for Automotive Radar Data [12.96486891333286]
  Radar sensors are an important part of the environment perception system of autonomous vehicles. One of the first steps during the processing of radar point clouds is often the detection of clutter. Another common objective is the semantic segmentation of moving road users. We show that our setup is highly effective and outperforms every existing network for semantic segmentation on the RadarScenes dataset.
  arXiv  Detail & Related papers  (2023-11-13T11:29:38Z)
• Zone Evaluation: Revealing Spatial Bias in Object Detection [69.59295428233844]
  A fundamental limitation of object detectors is that they suffer from "spatial bias" We present a new zone evaluation protocol, which measures the detection performance over zones. For the first time, we provide numerical results, showing that the object detectors perform quite unevenly across the zones.
  arXiv  Detail & Related papers  (2023-10-20T01:44:49Z)
• Unsupervised Adaptation from Repeated Traversals for Autonomous Driving [54.59577283226982]
  Self-driving cars must generalize to the end-user's environment to operate reliably. One potential solution is to leverage unlabeled data collected from the end-users' environments. There is no reliable signal in the target domain to supervise the adaptation process. We show that this simple additional assumption is sufficient to obtain a potent signal that allows us to perform iterative self-training of 3D object detectors on the target domain.
  arXiv  Detail & Related papers  (2023-03-27T15:07:55Z)
• Tackling Clutter in Radar Data -- Label Generation and Detection Using PointNet++ [10.113809521379022]
  We present two novel neural network setups for identifying clutter. The input data, network architectures and training configuration are adjusted specifically for this task. By applying it to existing data with object annotations and releasing its code, we effectively create the first freely available radar clutter data set.
  arXiv  Detail & Related papers  (2023-03-16T17:46:32Z)
• Bandit Quickest Changepoint Detection [55.855465482260165]
  Continuous monitoring of every sensor can be expensive due to resource constraints. We derive an information-theoretic lower bound on the detection delay for a general class of finitely parameterized probability distributions. We propose a computationally efficient online sensing scheme, which seamlessly balances the need for exploration of different sensing options with exploitation of querying informative actions.
  arXiv  Detail & Related papers  (2021-07-22T07:25:35Z)
• FMODetect: Robust Detection and Trajectory Estimation of Fast Moving Objects [110.29738581961955]
  We propose the first learning-based approach for detection and trajectory estimation of fast moving objects. The proposed method first detects all fast moving objects as a truncated distance function to the trajectory. For the sharp appearance estimation, we propose an energy minimization based deblurring.
  arXiv  Detail & Related papers  (2020-12-15T11:05:34Z)
• Slender Object Detection: Diagnoses and Improvements [74.40792217534]
  In this paper, we are concerned with the detection of a particular type of objects with extreme aspect ratios, namely textbfslender objects. For a classical object detection method, a drastic drop of $18.9%$ mAP on COCO is observed, if solely evaluated on slender objects.
  arXiv  Detail & Related papers  (2020-11-17T09:39:42Z)

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.