ReviewSensors (Basel, Switzerland)2022
A Survey on Modelling of Automotive Radar Sensors for Virtual Test and Validation of Automated Driving.
Review in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 29 citations in OpenAlex.
- Introducing a Development Method for Active Perception Sensor Simulations Using Continuous Verification and Validation.Sensors (Basel, Switzerland) · 2025Article
- Survey on Context-Aware Radio Frequency-Based Sensing.Sensors (Basel, Switzerland) · 2025Review
- Evaluation of Cluster Algorithms for Radar-Based Object Recognition in Autonomous and Assisted Driving.Sensors (Basel, Switzerland) · 2024Article
- An Overview of Millimeter-Wave Radar Modeling Methods for Autonomous Driving Simulation Applications.Sensors (Basel, Switzerland) · 2024Review
- Car Bumper Effects in ADAS Sensors at Automotive Radar Frequencies.Sensors (Basel, Switzerland) · 2023Article
- Deep Camera-Radar Fusion with an Attention Framework for Autonomous Vehicle Vision in Foggy Weather Conditions.Sensors (Basel, Switzerland) · 2023Article
- A Novel Approach for Simulation of Automotive Radar Sensors Designed for Systematic Support of Vehicle Development.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.