Autovision – A Run-time Reconfigurable MPSoC Architecture for Future Driver Assistance Systems (Autovision – Eine zur Laufzeit rekonfigurierbare MPSoC Architektur für zukünftige Fahrerassistenzsysteme)

2007 ◽  
Vol 49 (3) ◽  
Author(s):  
Christopher Claus ◽  
Walter Stechele ◽  
Andreas Herkersdorf
Keyword(s):  
Video Processing ◽  
Hardware Accelerator ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Dynamic Partial Reconfiguration ◽  
Chip Area ◽  
Assistance Systems ◽  
Run Time ◽  
Driving Conditions ◽  
On Chip

In this article the Autovision architecture is presented, a new Multi Processor System-on-Chip (MPSoC) architecture for future video-based driver assistance systems, using run-time reconfigurable hardware accelerator engines for video processing. According to various driving conditions (highway, city, sunlight, rain, tunnel entrance) different algorithms have to be used for video processing. These different algorithms require different hardware accelerator engines, which are loaded into the Autovision chip at run-time of the system, triggered by changing driving conditions. It was investigated how to use dynamic partial reconfiguration to load and operate the correct hardware accelerator engines in time, while removing unused engines in order to save precious chip area.

Development of a mid range automotive radar sensor for future driver assistance systems

2013 ◽  
Vol 5 (1) ◽  
pp. 15-23 ◽  
Author(s):  
Raik Schnabel ◽  
Raphael Hellinger ◽  
Dirk Steinbuch ◽  
Joachim Selinger ◽  
Michael Klar ◽  
...  
Keyword(s):  
Silicon Germanium ◽  
Urban Environments ◽  
Planar Antennas ◽  
Driver Assistance ◽  
Automotive Radar ◽  
Driver Assistance Systems ◽  
Radar Sensors ◽  
Assistance Systems ◽  
On Chip ◽  
Safety Applications

Radar sensors are key components of modern driver assistance systems. The application of such systems in urban environments for safety applications is the primary goal of the project “Radar on Chip for Cars” (RoCC). Major outcomes of this project will be presented and discussed in this contribution. These outcomes include the specification of radar sensors for future driver assistance systems, radar concepts, and integration technologies for silicon-germanium (SiGe) MMICs, as well as the development and evaluation of a system demonstrator. A radar architecture utilizing planar antennas and highly integrated components will be proposed and discussed with respect to system specifications. The developed system demonstrator will be evaluated in terms of key parameters such as field of view, distance, and angular separability. Finally, as an outlook a new mid range radar (MRR) will be introduced incorporating several concepts and technologies developed in this project.

scholarly journals Learning in AI Processor

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xinhua Wang ◽  
Weikang Wu
Keyword(s):  
Deep Learning ◽  
High Performance ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems ◽  
On Chip ◽  
Performance Computing ◽  
Analyze Data

AI processor, which can run artificial intelligence algorithms, is a state-of-the-art accelerator,in essence, to perform special algorithm in various applications. In particular,these are four AI applications: VR/AR smartphone games, high-performance computing, Advanced Driver Assistance Systems and IoT. Deep learning using convolutional neural networks (CNNs) involves embedding intelligence into applications to perform tasks and has achieved unprecedented accuracy [1]. Usually, the powerful multi-core processors and the on-chip tensor processing accelerator unit are prominent hardware features of deep learning AI processor. After data is collected by sensors, tools such as image processing technique, voice recognition and autonomous drone navigation, are adopted to pre-process and analyze data. In recent years, plenty of technologies associating with deep learning Al processor including cognitive spectrum sensing, computer vision and semantic reasoning become a focus in current research.

scholarly journals Advanced Driver Assistant Systems Focused on Pedestrians’ Safety: A User Experience Approach

2021 ◽  
Vol 13 (8) ◽  
pp. 4264
Author(s):  
Matúš Šucha ◽  
Ralf Risser ◽  
Kristýna Honzíčková
Keyword(s):  
Autonomous Vehicles ◽  
Qualitative Data ◽  
Expert Interviews ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistant Systems ◽  
Vehicle Technology ◽  
Assistance Systems ◽  
Technical Solutions ◽  
Near Future

Globally, pedestrians represent 23% of all road deaths. Many solutions to protect pedestrians are proposed; in this paper, we focus on technical solutions of the ADAS–Advanced Driver Assistance Systems–type. Concerning the interaction between drivers and pedestrians, we want to have a closer look at two aspects: how to protect pedestrians with the help of vehicle technology, and how pedestrians–but also car drivers–perceive and accept such technology. The aim of the present study was to analyze and describe the experiences, needs, and preferences of pedestrians–and drivers–in connection with ADAS, or in other words, how ADAS should work in such a way that it would protect pedestrians and make walking more relaxed. Moreover, we interviewed experts in the field in order to check if, in the near future, the needs and preferences of pedestrians and drivers can be met by new generations of ADAS. A combination of different methods, specifically, an original questionnaire, on-the-spot interviewing, and expert interviews, was used to collect data. The qualitative data was analyzed using qualitative text analysis (clustering and categorization). The questionnaire for drivers was answered by a total of 70 respondents, while a total of 60 pedestrians agreed to complete questionnaires concerning pedestrian safety. Expert interviews (five interviews) were conducted by means of personal interviews, approximately one hour in duration. We conclude that systems to protect pedestrians–to avoid collisions of cars with pedestrians–are considered useful by all groups, though with somewhat different implications. With respect to the features of such systems, the considerations are very heterogeneous, and experimentation is needed in order to develop optimal systems, but a decisive argument put forward by some of the experts is that autonomous vehicles will have to be programmed extremely defensively. Given this argument, we conclude that we will need more discussion concerning typical interaction situations in order to find solutions that allow traffic to work both smoothly and safely.

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