Frequency Modulated Continuous Wave Radar-Based Navigation Algorithm using Artificial Neural Network for Autonomous Driving

Robust functionality of autonomous driving vehicles relies on their ability to detect obstables and various scenarios on the road. This can be only achieved by applying robust, fast and efficient AI-based signal processing to radar data. In this work we present an empirical investigation on the question, whether one can apply artificial neural networks (ANNs) directly to frequency modulated continuous wave (FMCW) radar raw data. We show that preproceessing is not necessary if one has enough raw data.In our experiment we have data of 153 648 frames collected with a 60 GHz FMCW radar. We compare systematically the options of preprocessing the data using variational autoencoder, applying traditional preprocessing or omit data-preprocessing and apply ANN directly to raw data. We show that the last option results in 28% faster signal processing and highest accuracy. This is a promising result, since it enables edge computing and direct signal processing at the sensor level.

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“…Valtl J. et al [9] recorded multiple datasets with a FMCW radar on various scenarios with a recording setup as shown in Fig. 1.…”

Section: A Dataset Descriptionmentioning

confidence: 99%

Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Valtl

Mèndez

Mauro

et al. 2022

2022 29th International Conference on Systems, Signals and Image Processing (IWSSIP)

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“…The control of the pursuing car is taken over by an ANN. Details about the experimental setup can be taken from our previous works [24], [25].…”

Section: ) Regressionmentioning

confidence: 99%

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Valtl

Issakov

2022

IEEE Access

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This work is a result of a collaboration between the projects "KI-Flex" (project number 16ES1027), funded by the German Federal Ministry of Education and Research (BMBF) within the founding program Microelectronic from Germany innovation driver, the "KoSi" project, (project number 01MM20011C) funded by the German Federal Ministry for Digital and Transport (BMDV) within the funding program "Automatisiertes, Vernetztes Fahren" and the project "TEACHING" (project number 871385) founded by the Horizon 2020 program."ABSTRACT As more and more applications rely on Artificial Intelligence (AI), it is inevitable to explore the associated safety and security risks, especially for sensitive applications where physical integrity is at risk. One of the most interesting challenges that come with AI are adversarial attacks being a wellresearched problem in the visual domain, where a small change in the input data can cause the Neural Network (NN) to make an incorrect prediction. In the radar domain, AI is not that widespread yet but the results that AI applications produce are very promising, which is why more and more applications based on it are being used. This work presents three possible attack methods that are particularly suitable for the radar domain. The developed algorithms generate universal adversarial attack patches for all sorts of radar applications based on NN. The main goal of the algorithms, apart from the computation of universal patches, is the identification of sensitive areas in the raw radar data input which than can be examined more closely. To the best of our knowledge, this is the first work that deals with calculating universal patches on raw radar data, which is of great importance especially for interference analysis. The developed algorithms have been verified on two data sets. One in the field of autonomous driving where the attacks lead to a steering misprediction of up to 0.3 for the steering value which is within [-1,1], with the results also being successfully tested on a demonstrator. The other data set originated from a gesture recognition task, where the attacks decreased the accuracy, originally at 97.0% up to a minimum of 16.5%, which is slightly above 12.5% being the accuracy for a purely random prediction.INDEX TERMS adversarial attacks, artificial neural networks, autonomous vehicles, edge computing, object recognition, radar applications, real-time systems

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“…Radar sensors obtain information about the physical environment, such as the relative speed, distance, angle, and direction of motion of high-precision targets. Valtl et al [12] proposed an artificial neural network navigation algorithm for autonomous driving based on frequency-modulated continuous-wave radar. They employed 60-Hz frequency-modulated continuous-wave radar to obtain environmental information, gathered training data through vehicle manipulation, and used a deep CNN [13] to convert the information; steering and speed were set as the network outputs.…”

Section: Introductionmentioning

confidence: 99%

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

Shih

Lin

Jhang

2023

Sensors

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With the advancement of science and technology, the development and application of unmanned mobile vehicles (UMVs) have emerged as topics of crucial concern in the global industry. The development goals and directions of UMVs vary according to their industrial uses, which include navigation, autonomous driving, and environmental recognition; these uses have become the priority development goals of researchers in various fields. UMVs employ sensors to collect environmental data for environmental analysis and path planning. However, the analysis function of a single sensor is generally affected by natural environmental factors, resulting in poor identification results. Therefore, this study introduces fusion technology that employs heterogeneous sensors in the Ackerman UMV, leveraging the advantages of each sensor to enhance accuracy and stability in environmental detection and identification. This study proposes a fusion technique involving heterogeneous imaging and LiDAR (laser imaging, detection, and ranging) sensors in an Ackerman UMV. A camera is used to obtain real-time images, and YOLOv4-tiny and simple online real-time tracking are then employed to detect the location of objects and conduct object classification and object tracking. LiDAR is simultaneously used to obtain real-time distance information of detected objects. An inertial measurement unit is used to gather odometry information to determine the position of the Ackerman UMV. Static maps are created using simultaneous localization and mapping. When the user commands the Ackerman UMV to move to the target point, the vehicle control center composed of the robot operating system activates the navigation function through the navigation control module. The Ackerman UMV can reach the destination and instantly identify obstacles and pedestrians when in motion.

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Frequency Modulated Continuous Wave Radar-Based Navigation Algorithm using Artificial Neural Network for Autonomous Driving

Cited by 5 publications

References 11 publications

Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Investigation for the Need of Traditional Data-Preprocessing when Applying Artificial Neural Networks to FMCW-Radar Data

Universal Adversarial Attacks on the Raw Data From a Frequency Modulated Continuous Wave Radar

Ackerman Unmanned Mobile Vehicle Based on Heterogeneous Sensor in Navigation Control Application

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