10 research outputs found
Exploiting Sparsity in Automotive Radar Object Detection Networks
Having precise perception of the environment is crucial for ensuring the
secure and reliable functioning of autonomous driving systems. Radar object
detection networks are one fundamental part of such systems. CNN-based object
detectors showed good performance in this context, but they require large
compute resources. This paper investigates sparse convolutional object
detection networks, which combine powerful grid-based detection with low
compute resources. We investigate radar specific challenges and propose sparse
kernel point pillars (SKPP) and dual voxel point convolutions (DVPC) as
remedies for the grid rendering and sparse backbone architectures. We evaluate
our SKPP-DPVCN architecture on nuScenes, which outperforms the baseline by
5.89% and the previous state of the art by 4.19% in Car AP4.0. Moreover,
SKPP-DPVCN reduces the average scale error (ASE) by 21.41% over the baseline
Recent Advances in mmWave-Radar-Based Sensing, Its Applications, and Machine Learning Techniques: A Review
Human gesture detection, obstacle detection, collision avoidance, parking aids, automotive driving, medical, meteorological, industrial, agriculture, defense, space, and other relevant fields have all benefited from recent advancements in mmWave radar sensor technology. A mmWave radar has several advantages that set it apart from other types of sensors. A mmWave radar can operate in bright, dazzling, or no-light conditions. A mmWave radar has better antenna miniaturization than other traditional radars, and it has better range resolution. However, as more data sets have been made available, there has been a significant increase in the potential for incorporating radar data into different machine learning methods for various applications. This review focuses on key performance metrics in mmWave-radar-based sensing, detailed applications, and machine learning techniques used with mmWave radar for a variety of tasks. This article starts out with a discussion of the various working bands of mmWave radars, then moves on to various types of mmWave radars and their key specifications, mmWave radar data interpretation, vast applications in various domains, and, in the end, a discussion of machine learning algorithms applied with radar data for various applications. Our review serves as a practical reference for beginners developing mmWave-radar-based applications by utilizing machine learning techniques.publishedVersio
Automation of the UNICARagil Vehicles
The German research project UNICARagil is a collaboration between eight universities and six industrial partners funded by the Federal Ministry of Education and Research. It aims to develop innovative modular architectures and methods for new agile, automated vehicle concepts. This paper summarizes the automation approach of the driverless vehicle concept and its modular realization within the four demonstration vehicles to be built by the consortium. On-board each vehicle, this comprises sensor modules for environment perception and modelling, motion planning for normal driving and safe halts, as well as the respective control algorithms and base functionalities like precise localization. A control room and cloud functionalities provide off-board support to the vehicles, which are additionally addressed in this paper