Object Detection and 3D Estimation via an FMCW Radar Using A Fully Convolutional Network

This paper considers object detection and 3D estimation using an FMCW radar. The state-of-the-art deep learning framework is employed instead of using traditional signal processing. In preparing the radar training data, the ground truth of an object orientation in 3D space is provided by conducting image analysis, of which the images are obtained through a coupled camera to the radar device. To ensure successful training of a fully convolutional network (FCN), we propose a normalization method, which is found to be essential to be applied to the radar signal before feeding into the neural network. The system after proper training is able to first detect the presence of an object in an environment. If it does, the system then further produces an estimation of its 3D position. Experimental results show that the proposed system can be successfully trained and employed for detecting a car and further estimating its 3D position in a noisy environment.Comment: 5 page

Radars for Autonomous Driving: A Review of Deep Learning Methods and Challenges

Radar is a key component of the suite of perception sensors used for safe and reliable navigation of autonomous vehicles. Its unique capabilities include high-resolution velocity imaging, detection of agents in occlusion and over long ranges, and robust performance in adverse weather conditions. However, the usage of radar data presents some challenges: it is characterized by low resolution, sparsity, clutter, high uncertainty, and lack of good datasets. These challenges have limited radar deep learning research. As a result, current radar models are often influenced by lidar and vision models, which are focused on optical features that are relatively weak in radar data, thus resulting in under-utilization of radar's capabilities and diminishing its contribution to autonomous perception. This review seeks to encourage further deep learning research on autonomous radar data by 1) identifying key research themes, and 2) offering a comprehensive overview of current opportunities and challenges in the field. Topics covered include early and late fusion, occupancy flow estimation, uncertainty modeling, and multipath detection. The paper also discusses radar fundamentals and data representation, presents a curated list of recent radar datasets, and reviews state-of-the-art lidar and vision models relevant for radar research. For a summary of the paper and more results, visit the website: autonomous-radars.github.io

Practical classification of different moving targets using automotive radar and deep neural networks

In this work, the authors present results for classification of different classes of targets (car, single and multiple people, bicycle) using automotive radar data and different neural networks. A fast implementation of radar algorithms for detection, tracking, and micro-Doppler extraction is proposed in conjunction with the automotive radar transceiver TEF810X and microcontroller unit SR32R274 manufactured by NXP Semiconductors. Three different types of neural networks are considered, namely a classic convolutional network, a residual network, and a combination of convolutional and recurrent network, for different classification problems across the four classes of targets recorded. Considerable accuracy (close to 100% in some cases) and low latency of the radar pre-processing prior to classification (∼0.55 s to produce a 0.5 s long spectrogram) are demonstrated in this study, and possible shortcomings and outstanding issues are discussed

Weakly supervised deep learning method for vulnerable road user detection in FMCW radar

Millimeter-wave radar is currently the most effective automotive sensor capable of all-weather perception. In order to detect Vulnerable Road Users (VRUs) in cluttered radar data, it is necessary to model the time-frequency signal patterns of human motion, i.e. the micro-Doppler signature. In this paper we propose a spatio-temporal Convolutional Neural Network (CNN) capable of detecting VRUs in cluttered radar data. The main contribution is a weakly supervised training method which uses abundant, automatically generated labels from camera and lidar for training the model. The input to the network is a tensor of temporally concatenated range-azimuth-Doppler arrays, while the ground truth is an occupancy grid formed by objects detected jointly in-camera images and lidar. Lidar provides accurate ranging ground truth, while camera information helps distinguish between VRUs and background. Experimental evaluation shows that the CNN model has superior detection performance compared to classical techniques. Moreover, the model trained with imperfect, weak supervision labels outperforms the one trained with a limited number of perfect, hand-annotated labels. Finally, the proposed method has excellent scalability due to the low cost of automatic annotation