A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving

Abstract

Radar sensors were among the first perceptual sensors used for automated driving. Although several other technologies such as lidar, camera, and ultrasonic sensors are available, radar sensors have maintained and will continue to maintain their importance due to their reliability in adverse weather conditions. Virtual methods are being developed for verification and validation of automated driving functions to reduce the time and cost of testing. Due to the complexity of modelling high-frequency wave propagation and signal processing and perception algorithms, sensor models that seek a high degree of accuracy are challenging to simulate. Therefore, a variety of different modelling approaches have been presented in the last two decades. This paper comprehensively summarises the heterogeneous state of the art in radar sensor modelling. Instead of a technology-oriented classification as introduced in previous review articles, we present a classification of how these models can be used in vehicle development by using the V-model originating from software development. Sensor models are divided into operational, functional, technical, and individual models. The application and usability of these models along the development process are summarised in a comprehensive tabular overview, which is intended to support future research and development at the vehicle level and will be continuously updated.

A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development

“…As can be seen, the requirement for simulation efficiency in sensor modelling was met, as neither increased computing power nor special code optimisation was required to run the simulation faster than real time for all three in Section 1 . For more details about sensor classification, please refer to [ 2 ].…”

“…To support X-in-the-loop testing methods during the vehicle development process, a wide range of commercial or open-source simulation software is available to the automotive industry. Referring to our previous work [ 2 ], some examples are given: in [ 5 ]: TASS-PreScan, dSpace-ASM, in [ 3 ]: TESIS Dyna4-Driver Assistance, MathWorks-ADAS Toolbox, in [ 6 ]: CARLA, AirSim, DeepDrive, Udacity, or in [ 7 ]: CarMaker from IPG Automotive GmbH., VIRES-VTD. These software packages provide a variety of interfaces for modelling perception sensors at different levels of complexity, but their parallel use is often limited, whereas in real application data, the fusion of multiple sensors is state of the art.…”

“…Due to the above-mentioned need for virtual V&V, sensor models of different complexity and accuracy are required at different stages of vehicle development to fully satisfy the respective requirements. Therefore, in a previous work [ 2 ], we introduced a novel method for classifying radar sensor models found in the literature according to the stages of the vehicle development process. Based on the vehicle manufacturers’ requirements, we have also assigned them to these stages depending on their applicability.…”

From Traffic Sensor Data To Semantic Traffic Descriptions: The Test Area Autonomous Driving Baden-Württemberg Dataset (TAF-BW Dataset)

“…So far, hardly any ontologies for autonomous driving have been found, which focus on the detailed, physical level. However, when extending the search to sensor-based testing, many different approaches are discussed, especially in the radar domain [44].…”

Deep Camera-Radar Fusion with Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions

“…2 of 27 detections are far too sparse [13,14]. The sparse nature of radar point clouds collected with many vehicular radars (usually 64 points or less) might explain this [15].…”