Validation of Automated Driving Functions Using Real and Synthetic Radar Data

ATZ worldwide ◽  
2021 ◽  
Vol 124 (1) ◽  
pp. 36-41
Author(s):  
Patrick Schnöll ◽  
Axel Schneider ◽  
Stephan Hakuli ◽  
Andreas Höfer
Keyword(s):  
Radar Data ◽  
Automated Driving

scholarly journals Radar Data Integrity Verification Using 2D QIM-Based Data Hiding

Sensors ◽  
2020 ◽  
Vol 20 (19) ◽  
pp. 5530
Author(s):  
Raghu Changalvala ◽  
Brandon Fedoruk ◽  
Hafiz Malik
Keyword(s):  
Sensor Fusion ◽  
Data Hiding ◽  
Data Integrity ◽  
Radar Data ◽  
Sensor Data ◽  
Area Network ◽  
Automated Driving ◽  
Fusion Algorithm ◽  
Integrity Verification ◽  
Internal Networks

The modern-day vehicle is evolved in a cyber-physical system with internal networks (controller area network (CAN), Ethernet, etc.) connecting hundreds of micro-controllers. From the traditional core vehicle functions, such as vehicle controls, infotainment, and power-train management, to the latest developments, such as advanced driver assistance systems (ADAS) and automated driving features, each one of them uses CAN as their communication network backbone. Automated driving and ADAS features rely on data transferred over the CAN network from multiple sensors mounted on the vehicle. Verifying the integrity of the sensor data is essential for the safety and security of occupants and the proper functionality of these applications. Though the CAN interface ensures reliable data transfer, it lacks basic security features, including message authentication, which makes it vulnerable to a wide array of attacks, including spoofing, replay, DoS, etc. Using traditional cryptography-based methods to verify the integrity of data transmitted over CAN interfaces is expected to increase the computational complexity, latency, and overall cost of the system. In this paper, we propose a light-weight alternative to verify the sensor data’s integrity for vehicle applications that use CAN networks for data transfers. To this end, a framework for 2-dimensional quantization index modulation (2D QIM)-based data hiding is proposed to achieve this goal. Using a typical radar sensor data transmission scenario in an autonomous vehicle application, we analyzed the performance of the proposed framework regarding detecting and localizing the sensor data tampering. The effects of embedding-induced distortion on the applications using the radar data were studied through a sensor fusion algorithm. It was observed that the proposed framework offers the much-needed data integrity verification without compromising on the quality of sensor fusion data and is implemented with low overall design complexity. This proposed framework can also be used on any physical network interface other than CAN, and it offers traceability to in-vehicle data beyond the scope of the in-vehicle applications.

scholarly journals MMW Radar-Based Technologies in Autonomous Driving: A Review

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7283
Author(s):  
Taohua Zhou ◽  
Mengmeng Yang ◽  
Kun Jiang ◽  
Henry Wong ◽  
Diange Yang
Keyword(s):  
Autonomous Vehicles ◽  
Low Cost ◽  
Rapid Development ◽  
Autonomous Driving ◽  
Radar Data ◽  
Intelligent Vehicles ◽  
Data Types ◽  
Automated Driving ◽  
Driving Assistance ◽  
High Level

With the rapid development of automated vehicles (AVs), more and more demands are proposed towards environmental perception. Among the commonly used sensors, MMW radar plays an important role due to its low cost, adaptability In different weather, and motion detection capability. Radar can provide different data types to satisfy requirements for various levels of autonomous driving. The objective of this study is to present an overview of the state-of-the-art radar-based technologies applied In AVs. Although several published research papers focus on MMW Radars for intelligent vehicles, no general survey on deep learning applied In radar data for autonomous vehicles exists. Therefore, we try to provide related survey In this paper. First, we introduce models and representations from millimeter-wave (MMW) radar data. Secondly, we present radar-based applications used on AVs. For low-level automated driving, radar data have been widely used In advanced driving-assistance systems (ADAS). For high-level automated driving, radar data is used In object detection, object tracking, motion prediction, and self-localization. Finally, we discuss the remaining challenges and future development direction of related studies.

Potentialities of the comparison of «Sich-1M» side looking radar data and foreign satellite information

2004 ◽  
Vol 10 (2-3) ◽  
pp. 93-100
Author(s):  
V.V. Malynovskyi ◽  
◽  
V.P. Zubko ◽  
V.V. Pustovoitenko ◽  
◽  
...  
Keyword(s):  
Radar Data ◽  
Satellite Information ◽  
Side Looking Radar

INTELLIGENT ANALYSIS OF RADAR DATA BASED ON FUZZY TRANSFORMS

2018 ◽  
Vol 77 (15) ◽  
pp. 1321-1329 ◽  
Author(s):  
S.V. Solonskaya ◽  
V. V. Zhirnov
Keyword(s):  
Radar Data ◽  
Intelligent Analysis

Reconstructing Properties of Subsurface from Ground-Penetrating Radar Data

PIERS Online ◽  
2006 ◽  
Vol 2 (6) ◽  
pp. 567-572
Author(s):  
Hui Zhou ◽  
Dongling Qiu ◽  
Takashi Takenaka
Keyword(s):  
Ground Penetrating Radar ◽  
Radar Data ◽  
Ground Penetrating
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