Multimodal Detection of Unknown Objects on Roads for Autonomous Driving

• URL: http://arxiv.org/abs/2205.01414v1
• Date: Tue, 3 May 2022 10:58:41 GMT
• Title: Multimodal Detection of Unknown Objects on Roads for Autonomous Driving
• Authors: Daniel Bogdoll and Enrico Eisen and Maximilian Nitsche and Christin Scheib and J. Marius Z\"ollner
• Abstract summary: We propose a novel pipeline to detect unknown objects. We make use of lidar and camera data by combining state-of-the art detection models in a sequential manner.
• Score: 4.3310896118860445
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Tremendous progress in deep learning over the last years has led towards a future with autonomous vehicles on our roads. Nevertheless, the performance of their perception systems is strongly dependent on the quality of the utilized training data. As these usually only cover a fraction of all object classes an autonomous driving system will face, such systems struggle with handling the unexpected. In order to safely operate on public roads, the identification of objects from unknown classes remains a crucial task. In this paper, we propose a novel pipeline to detect unknown objects. Instead of focusing on a single sensor modality, we make use of lidar and camera data by combining state-of-the art detection models in a sequential manner. We evaluate our approach on the Waymo Open Perception Dataset and point out current research gaps in anomaly detection.

Related papers

• End-to-End 3D Object Detection using LiDAR Point Cloud [0.0]
  We present an approach wherein, using a novel encoding of the LiDAR point cloud we infer the location of different classes near the autonomous vehicles. The output is predictions about the location and orientation of objects in the scene in form of 3D bounding boxes and labels of scene objects.
  arXiv  Detail & Related papers  (2023-12-24T00:52:14Z)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• End-to-end Autonomous Driving using Deep Learning: A Systematic Review [0.0]
  End-to-end autonomous driving is a fully differentiable machine learning system that takes raw sensor input data and other metadata as prior information and directly outputs the ego vehicle's control signals or planned trajectories. This paper attempts to systematically review all recent Machine Learning-based techniques to perform this end-to-end task, including, but not limited to, object detection, semantic scene understanding, object tracking, trajectory predictions, trajectory planning, vehicle control, social behavior, and communications.
  arXiv  Detail & Related papers  (2023-08-27T17:43:58Z)
• Recent Advancements in End-to-End Autonomous Driving using Deep Learning: A Survey [9.385936248154987]
  End-to-End driving is a promising paradigm as it circumvents the drawbacks associated with modular systems. Recent developments in End-to-End autonomous driving are analyzed, and research is categorized based on underlying principles. This paper assesses the state-of-the-art, identifies challenges, and explores future possibilities.
  arXiv  Detail & Related papers  (2023-07-10T07:00:06Z)
• Unsupervised Self-Driving Attention Prediction via Uncertainty Mining and Knowledge Embedding [51.8579160500354]
  We propose an unsupervised way to predict self-driving attention by uncertainty modeling and driving knowledge integration. Results show equivalent or even more impressive performance compared to fully-supervised state-of-the-art approaches.
  arXiv  Detail & Related papers  (2023-03-17T00:28:33Z)
• Motion Inspired Unsupervised Perception and Prediction in Autonomous Driving [29.731790562352344]
  This paper pioneers a novel and challenging direction, i.e., training perception and prediction models to understand open-set moving objects. Our proposed framework uses self-learned flow to trigger an automated meta labeling pipeline to achieve automatic supervision. We show that our approach generates highly promising results in open-set 3D detection and trajectory prediction.
  arXiv  Detail & Related papers  (2022-10-14T18:55:44Z)
• 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)
• SODA10M: Towards Large-Scale Object Detection Benchmark for Autonomous Driving [94.11868795445798]
  We release a Large-Scale Object Detection benchmark for Autonomous driving, named as SODA10M, containing 10 million unlabeled images and 20K images labeled with 6 representative object categories. To improve diversity, the images are collected every ten seconds per frame within 32 different cities under different weather conditions, periods and location scenes. We provide extensive experiments and deep analyses of existing supervised state-of-the-art detection models, popular self-supervised and semi-supervised approaches, and some insights about how to develop future models.
  arXiv  Detail & Related papers  (2021-06-21T13:55:57Z)
• One Million Scenes for Autonomous Driving: ONCE Dataset [91.94189514073354]
  We introduce the ONCE dataset for 3D object detection in the autonomous driving scenario. The data is selected from 144 driving hours, which is 20x longer than the largest 3D autonomous driving dataset available. We reproduce and evaluate a variety of self-supervised and semi-supervised methods on the ONCE dataset.
  arXiv  Detail & Related papers  (2021-06-21T12:28:08Z)
• 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)
• PLOP: Probabilistic poLynomial Objects trajectory Planning for autonomous driving [8.105493956485583]
  We use a conditional imitation learning algorithm to predict trajectories for ego vehicle and its neighbors. Our approach is computationally efficient and relies only on on-board sensors. We evaluate our method offline on the publicly available dataset nuScenes.
  arXiv  Detail & Related papers  (2020-03-09T16:55:07Z)

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.