Automotive Object Detection on Highly Compressed Range-Beam-Doppler Radar Data

Meyer, Michaël; Nekkah, Sherif; Kuschk, Georg; Tomforde, Sven

doi:10.23919/eurad54643.2022.9924839

Cited by 30 publications

(39 citation statements)

References 4 publications

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“…Because of its advantages, camera-radar fusion is receiving increasing attention in the literature. Many works already consider this multi-modal perception in detection tasks [10]- [13], [84]- [91], while few of them apply the sensor fusion approach in segmentation tasks [9]. Therefore, there is plenty of room for exploring camera-radar fusion in vehicular perception.…”

Section: Sensor Fusionmentioning

confidence: 99%

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Manfio¹,

Osório²

2023

Preprint

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One of the main paths towards the reduction of traffic accidents is the increase in vehicle safety through driver assistance systems or even systems with a complete level of autonomy. In these types of systems, tasks such as obstacle detection and segmentation, especially the Deep Learning-based ones, play a fundamental role in scene understanding for correct and safe navigation. Besides that, the wide variety of sensors in vehicles nowadays provides a rich set of alternatives for improvement in the robustness of perception in challenging situations, such as navigation under lighting and weather adverse conditions. Despite the current focus given to the subject, the literature lacks studies on radar-based and radar-camera fusionbased perception. Hence, 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. Additionally, 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.

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Section: Sensor Fusionmentioning

confidence: 99%

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Manfio¹,

Osório²

2023

Preprint

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“…In addition, the Visual Relationship Dataset (VRD) [6], Visual Genome Dataset (VG) [58], Visually-Relevant Relationships Dataset (VrR-VG) [59], UnRel Dataset [60], HCVRD dataset [61] and others have appeared, with increasing numbers of images, object annotations and relationship annotations. Within the self-driving community, many excellent, large-scale dataset have also emerged [62]- [68]. However, our Road Scene Graph dataset would be the first focused on the semantic relationships among vehicles, pedestrians and other actors in traffic scenes.…”

Section: B Scene Graph Predictionmentioning

confidence: 99%

RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior

Tian

Carballo

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore, in this work, we propose a Road Scene Graphs-Graph Convolutional Network (RSG-GCN) as a novel, graphbased model for predicting the topological graph structure of a given traffic scene. The status of the actors and HD map information are integrated as prior knowledge, allowing the edges linking the actor nodes to capture potential semantic relationships, such as "vehicle approaching pedestrian" and "pedestrian waiting at intersection". To train this model, we created our own RSG dataset, as well as a relational dataset and benchmark derived from nuScenes. Our extensive range of experiments demonstrate that our model can more accurately predict semantic relationships and behavior in a given traffic scene than other popular traffic scene prediction models. In particular, regarding the use of HD map prior knowledge, we found that the resulting increase in accuracy significantly outweighs performance loss caused by the increase in graph size. The downstream applications of RSG include traffic scene retrieval and synthetic traffic scene generation, which are briefly described.

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“…Collecting, curating, and labeling a multimodal autonomous driving dataset is an involved procedure, and it requires considerable amount of human effort, funds, time, and planning. Nevertheless, collective efforts to produce such datasets yielded some immensely useful and publicly available datasets that advanced autonomous driving research, such as KITTI dataset, 16 nuScense dataset, 17 Astyx Dataset, 18 Argoverse dataset, 19 and Waymo dataset. 20 While these datasets provide ample amounts of multisensor readings, with labels for object detection, tracking, semantic segmentation, and visual odometry, most of these datasets were recorded in clear weather conditions.…”

Section: Datasets For Autonomous Drivingmentioning

confidence: 99%

Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks

2022

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Autonomous vehicles rely on perceiving the road environment through a perception pipeline fed by a variety of sensor modalities, including camera, lidar, radar, infrared, gated camera, and ultrasonic. The vehicle computer must perform sensor fusion reliably in various sensor signals degrading environments, which can be performed parametrically or using machine learning. We study sensor fusion of a forward-facing camera and a lidar sensor using convolutional neural networks (CNNs), for drivable area detection in winter driving, where the roads are partially covered with snow, tire tracks obscuring lane markers. Seven fusion models are developed and evaluated for their ability to classify pixels into two classes: drivable and nondrivable. A total of seven models are designed and tested, including camera only, lidar only, early fusion, (three types of) intermediate fusion, and late fusion. The models have accuracies between 84% and 89%, with runtimes between 23 and 61 ms. To select the best model from this group, we introduce a unique metric, named normalized accuracy runtime (NAR) score. A network in a group has a higher NAR score, the more accurate it is, and the shorter its runtime is. The models are evaluated on a winter driving DENSE dataset. Camera and lidar fog noise are synthesized to examine model robustness.

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Automotive Object Detection on Highly Compressed Range-Beam-Doppler Radar Data

Cited by 30 publications

References 4 publications

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

Camera-Radar Perception for Autonomous Vehicles and ADAS: Concepts, Datasets and Metrics

RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior

Camera–Lidar sensor fusion for drivable area detection in winter weather using convolutional neural networks

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