Road user detection with convolutional neural networks: An application to the autonomous shuttle WEpod

Gaisser, Floris; Jonker, Pieter

doi:10.23919/mva.2017.7986800

Cited by 13 publications

(5 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A recent trend in machine learning is the incorporation of preprocessing and classification steps in a single convolutional neural network (CNN). This has also been done for automotive radar classification, e.g., [8] or [9]. Due to the aforementioned reasons, the LSTM approach is preferred, here.…”

Section: Introductionmentioning

confidence: 99%

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Scheiner

Appenrodt

Dickmann

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

Radar-based road user classification is an important yet still challenging task towards autonomous driving applications. The resolution of conventional automotive radar sensors results in a sparse data representation which is tough to recover by subsequent signal processing. In this article, classifier ensembles originating from a one-vs-one binarization paradigm are enriched by one-vs-all correction classifiers. They are utilized to efficiently classify individual traffic participants and also identify hidden object classes which have not been presented to the classifiers during training. For each classifier of the ensemble an individual feature set is determined from a total set of 98 features. Thereby, the overall classification performance can be improved when compared to previous methods and, additionally, novel classes can be identified much more accurately. Furthermore, the proposed structure allows to give new insights in the importance of features for the recognition of individual classes which is crucial for the development of new algorithms and sensor requirements.

show abstract

Section: Introductionmentioning

confidence: 99%

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Scheiner

Appenrodt

Dickmann

et al. 2019

2019 IEEE Intelligent Vehicles Symposium (IV)

View full text Add to dashboard Cite

show abstract

“…Decision-level fusion scheme: mainly the detection results of mmWave radar and vision sensors are fused, both sensors are unified into the same reference system and the radar feature information is used to localize the area to be selected. These regions to be selected will be used as inputs for image-based deep learning [4] and machine learning [5] for subsequent target detection and classification [6] [7] . However, decision-level fusion has a very limited degree of detection performance improvement and occupies huge computational resources.…”

Section: Related Workmentioning

confidence: 99%

Obstacle detect based on improved neural network for fusion of radar and visual information

Luo

Zhang

2023

International Conference on Computer Vision, Application, and Algorithm (CVAA 2022)

View full text Add to dashboard Cite

Image target detection technology using deep learning has been fully developed in recent years, but in harsh and complex environments such as rain, snow, and darkness, the information collected by the image sensor will have a lot of noise, and it is easy to lose information about small distant targets and fuzzy targets, resulting in false detection and missed detection. Millimeter-wave(mmWave) radar uses electromagnetic waves for obstacle detection, and the detection performance in harsh environments basically does not degrade, in order to solve the situation that the detection accuracy is significantly reduced when single sensor environmental sensing technology is applied to complex scenes. In this paper, an enhanced neural network model for feature fusion is proposed by fusing radar and camera sensor features and combining them with an improved YOLO-S neural network. Firstly, the model is designed to convert the radar point cloud into image form while applying the BiFPN structure with integrated CA attention mechanism to the YOLOX-S neural network. By connecting radar features with image features and combining radar features with spatial attention weights, the useless information in the extracted features is suppressed while the key information is enhanced and the sensor features are complementary. The experimental results show that the fusion-enhanced network designed in this paper fully integrates the features of both sensors and provides better detection results and stronger robustness in detecting small distant objects and objects in dark and low-light environments.

show abstract

“…In data-level fusion (also called low-level), the raw data from radar and vision sensors are fused with deep learning models [ 47 , 153 , 157 , 158 , 159 , 160 , 161 ]. It consists of two steps: first, the target objects are predicted with the radar sensor.…”

Section: Deep Learning-based Multi-sensor Fusion Of Radar and Camementioning

confidence: 99%

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Abdu

Zhang

et al. 2021

Sensors

View full text Add to dashboard Cite

The progress brought by the deep learning technology over the last decade has inspired many research domains, such as radar signal processing, speech and audio recognition, etc., to apply it to their respective problems. Most of the prominent deep learning models exploit data representations acquired with either Lidar or camera sensors, leaving automotive radars rarely used. This is despite the vital potential of radars in adverse weather conditions, as well as their ability to simultaneously measure an object’s range and radial velocity seamlessly. As radar signals have not been exploited very much so far, there is a lack of available benchmark data. However, recently, there has been a lot of interest in applying radar data as input to various deep learning algorithms, as more datasets are being provided. To this end, this paper presents a survey of various deep learning approaches processing radar signals to accomplish some significant tasks in an autonomous driving application, such as detection and classification. We have itemized the review based on different radar signal representations, as it is one of the critical aspects while using radar data with deep learning models. Furthermore, we give an extensive review of the recent deep learning-based multi-sensor fusion models exploiting radar signals and camera images for object detection tasks. We then provide a summary of the available datasets containing radar data. Finally, we discuss the gaps and important innovations in the reviewed papers and highlight some possible future research prospects.

show abstract

Road user detection with convolutional neural networks: An application to the autonomous shuttle WEpod

Cited by 13 publications

References 14 publications

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Radar-based Road User Classification and Novelty Detection with Recurrent Neural Network Ensembles

Obstacle detect based on improved neural network for fusion of radar and visual information

Application of Deep Learning on Millimeter-Wave Radar Signals: A Review

Contact Info

Product

Resources

About