Sensor fusion: How fused data can support the development of automated driving in virtual environment and provide data for guiding vehicles

2020 ◽  
Author(s):  
Siegfried Seebacher ◽  
Jacqueline Erhart ◽  
Arnold Präsent
Keyword(s):  
Virtual Environment ◽  
Sensor Fusion ◽  
Automated Driving

scholarly journals Multi-Sensor Fusion in Automated Driving: A Survey

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 2847-2868 ◽  
Author(s):  
Zhangjing Wang ◽  
Yu Wu ◽  
Qingqing Niu
Keyword(s):  
Sensor Fusion ◽  
Automated Driving

Multi-sensor fusion for Automated Driving: Selecting model and optimizing on Embedded platform

2018 ◽  
Vol 2018 (17) ◽  
pp. 256-1-256-5 ◽  
Author(s):  
Shyam Jagannathan ◽  
Mihir Mody ◽  
Jason Jones ◽  
Pramod Swami ◽  
Deepak Poddar
Keyword(s):  
Sensor Fusion ◽  
Automated Driving ◽  
Embedded Platform

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 Sensor and Sensor Fusion Technology in Autonomous Vehicles: A Review

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2140
Author(s):  
De Jong Yeong ◽  
Gustavo Velasco-Hernandez ◽  
John Barry ◽  
Joseph Walsh
Keyword(s):  
Object Detection ◽  
Sensor Fusion ◽  
Autonomous Vehicles ◽  
Autonomous Driving ◽  
Obstacle Detection ◽  
Future Research ◽  
Sensor Calibration ◽  
Automated Driving ◽  
Detection Techniques ◽  
Technical Performance

With the significant advancement of sensor and communication technology and the reliable application of obstacle detection techniques and algorithms, automated driving is becoming a pivotal technology that can revolutionize the future of transportation and mobility. Sensors are fundamental to the perception of vehicle surroundings in an automated driving system, and the use and performance of multiple integrated sensors can directly determine the safety and feasibility of automated driving vehicles. Sensor calibration is the foundation block of any autonomous system and its constituent sensors and must be performed correctly before sensor fusion and obstacle detection processes may be implemented. This paper evaluates the capabilities and the technical performance of sensors which are commonly employed in autonomous vehicles, primarily focusing on a large selection of vision cameras, LiDAR sensors, and radar sensors and the various conditions in which such sensors may operate in practice. We present an overview of the three primary categories of sensor calibration and review existing open-source calibration packages for multi-sensor calibration and their compatibility with numerous commercial sensors. We also summarize the three main approaches to sensor fusion and review current state-of-the-art multi-sensor fusion techniques and algorithms for object detection in autonomous driving applications. The current paper, therefore, provides an end-to-end review of the hardware and software methods required for sensor fusion object detection. We conclude by highlighting some of the challenges in the sensor fusion field and propose possible future research directions for automated driving systems.

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