Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning

Garcia, Javier Martinez; Prophet, Robert; Michel, Juan Carlos Fuentes; Ebelt, Randolf; Vossiek, Martin; Weber, Ingo

doi:10.1109/icmim.2019.8726704

Cited by 19 publications

(10 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The scene structure and relationship between detections are important cues to identify ghost objects. Garcia et al [200] suggest the occupancy grid map can provide the information of the scene structure. Therefore, they used the occupancy grid map and the list of moving objects as inputs to FCN, to predict a heat map of moving ghost detections.…”

Section: Ghost Object Detectionmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

With recent developments, the performance of automotive radar has improved significantly. The next generation of 4D radar can achieve imaging capability in the form of high-resolution point clouds. In this context, we believe that the era of deep learning for radar perception has arrived. However, studies on radar deep learning are spread across different tasks, and a holistic overview is lacking. This review paper attempts to provide a big picture of the deep radar perception stack, including signal processing, datasets, labelling, data augmentation, and downstream tasks such as depth and velocity estimation, object detection, and sensor fusion. For these tasks, we focus on explaining how the network structure is adapted to radar domain knowledge. In particular, we summarise three overlooked challenges in deep radar perception, including multi-path effects, uncertainty problems, and adverse weather effects, and present some attempts to solve them.

show abstract

Section: Ghost Object Detectionmentioning

confidence: 99%

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Zhou

Liu

Zhao

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…Hence, errors in the first algorithm step are limited to the rare case of purely tangential moving targets. As a modification of this approach, [10] presents a detection algorithm based on deep learning. Instead of handcrafted features, the algorithm uses an occupancy grid map and a map of moving targets as input.…”

Section: Related Workmentioning

confidence: 99%

Anomaly Detection in Radar Data Using PointNets

Griebel¹,

Authaler²,

Horn³

et al. 2021

Preprint

View full text Add to dashboard Cite

For autonomous driving, radar is an important sensor type. On the one hand, radar offers a direct measurement of the radial velocity of targets in the environment. On the other hand, in literature, radar sensors are known for their robustness against several kinds of adverse weather conditions. However, on the downside, radar is susceptible to ghost targets or clutter which can be caused by several different causes, e.g., reflective surfaces in the environment. Ghost targets, for instance, can result in erroneous object detections. To this end, it is desirable to identify anomalous targets as early as possible in radar data. In this work, we present an approach based on PointNets to detect anomalous radar targets. Modifying the PointNet-architecture driven by our task, we developed a novel grouping variant which contributes to a multi-form grouping module. Our method is evaluated on a real-world dataset in urban scenarios and shows promising results for the detection of anomalous radar targets.

show abstract

“…While multiple models are tested, the best performance is achieved using a random forest [4]. The same authors explore a different approach in [5]. Here, a convolutional neural network is employed.…”

Section: Related Workmentioning

confidence: 99%

Fast Rule-Based Clutter Detection in Automotive Radar Data

Kopp¹,

Kellner²,

Piroli³

et al. 2021

Preprint

View full text Add to dashboard Cite

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 semiautomatic 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.

show abstract

Identification of Ghost Moving Detections in Automotive Scenarios with Deep Learning

Cited by 19 publications

References 3 publications

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Towards Deep Radar Perception for Autonomous Driving: Datasets, Methods, and Challenges

Anomaly Detection in Radar Data Using PointNets

Fast Rule-Based Clutter Detection in Automotive Radar Data

Contact Info

Product

Resources

About