Object Detection in Autonomous Vehicles: Status and Open Challenges

• URL: http://arxiv.org/abs/2201.07706v1
• Date: Wed, 19 Jan 2022 16:45:16 GMT
• Title: Object Detection in Autonomous Vehicles: Status and Open Challenges
• Authors: Abhishek Balasubramaniam, Sudeep Pasricha
• Abstract summary: Object detection is a computer vision task that has become an integral part of many consumer applications today. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.
• Score: 4.226118870861363
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Object detection is a computer vision task that has become an integral part of many consumer applications today such as surveillance and security systems, mobile text recognition, and diagnosing diseases from MRI/CT scans. Object detection is also one of the critical components to support autonomous driving. Autonomous vehicles rely on the perception of their surroundings to ensure safe and robust driving performance. This perception system uses object detection algorithms to accurately determine objects such as pedestrians, vehicles, traffic signs, and barriers in the vehicle's vicinity. Deep learning-based object detectors play a vital role in finding and localizing these objects in real-time. This article discusses the state-of-the-art in object detectors and open challenges for their integration into autonomous vehicles.

Related papers

• Real-time Traffic Object Detection for Autonomous Driving [5.780326596446099]
  Modern computer vision techniques tend to prioritize accuracy over efficiency. Existing object detectors are far from being real-time. We propose a more suitable alternative that incorporates real-time requirements.
  arXiv  Detail & Related papers  (2024-01-31T19:12:56Z)
• Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics [77.34726150561087]
  This work aims to carry out a study on the current scenario of camera and radar-based perception for ADAS and autonomous vehicles. Concepts and characteristics related to both sensors, as well as to their fusion, are presented. We give an overview of the Deep Learning-based detection and segmentation tasks, and the main datasets, metrics, challenges, and open questions in vehicle perception.
  arXiv  Detail & Related papers  (2023-03-08T00:48:32Z)
• 3D Object Detection for Autonomous Driving: A Comprehensive Survey [48.30753402458884]
  3D object detection, which intelligently predicts the locations, sizes, and categories of the critical 3D objects near an autonomous vehicle, is an important part of a perception system. This paper reviews the advances in 3D object detection for autonomous driving.
  arXiv  Detail & Related papers  (2022-06-19T19:43:11Z)
• CODA: A Real-World Road Corner Case Dataset for Object Detection in Autonomous Driving [117.87070488537334]
  We introduce a challenging dataset named CODA that exposes this critical problem of vision-based detectors. The performance of standard object detectors trained on large-scale autonomous driving datasets significantly drops to no more than 12.8% in mAR. We experiment with the state-of-the-art open-world object detector and find that it also fails to reliably identify the novel objects in CODA.
  arXiv  Detail & Related papers  (2022-03-15T08:32:56Z)
• A Quality Index Metric and Method for Online Self-Assessment of Autonomous Vehicles Sensory Perception [164.93739293097605]
  We propose a novel evaluation metric, named as the detection quality index (DQI), which assesses the performance of camera-based object detection algorithms. We have developed a superpixel-based attention network (SPA-NET) that utilizes raw image pixels and superpixels as input to predict the proposed DQI evaluation metric.
  arXiv  Detail & Related papers  (2022-03-04T22:16:50Z)
• Evaluating Object (mis)Detection from a Safety and Reliability Perspective: Discussion and Measures [1.8492669447784602]
  We propose new object detection measures that reward the correct identification of objects that are most dangerous and most likely to affect driving decisions. We apply our model on the recent autonomous driving dataset nuScenes, and we compare nine object detectors. Results show that, in several settings, object detectors that perform best according to the nuScenes ranking are not the preferable ones when the focus is shifted on safety and reliability.
  arXiv  Detail & Related papers  (2022-03-04T09:31:20Z)
• Provident Vehicle Detection at Night for Advanced Driver Assistance Systems [3.7468898363447654]
  We present a complete system capable of providingntly detect oncoming vehicles at nighttime based on their caused light artifacts. We quantify the time benefit that the provident vehicle detection system provides compared to an in-production computer vision system.
  arXiv  Detail & Related papers  (2021-07-23T15:27:17Z)
• Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection [55.12894776039135]
  State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit to domain idiosyncrasies. We propose a novel learning approach that drastically reduces this gap by fine-tuning the detector on pseudo-labels in the target domain. We show, on five autonomous driving datasets, that fine-tuning the detector on these pseudo-labels substantially reduces the domain gap to new driving environments.
  arXiv  Detail & Related papers  (2021-03-26T01:18:11Z)
• Physically Realizable Adversarial Examples for LiDAR Object Detection [72.0017682322147]
  We present a method to generate universal 3D adversarial objects to fool LiDAR detectors. In particular, we demonstrate that placing an adversarial object on the rooftop of any target vehicle to hide the vehicle entirely from LiDAR detectors with a success rate of 80%. This is one step closer towards safer self-driving under unseen conditions from limited training data.
  arXiv  Detail & Related papers  (2020-04-01T16:11:04Z)

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.