Standardized Self-diagnostic Sensing Systems for Highly Automated Driving (S4DRIVE)

Themes: Radar for Remote Sensing and Targets Characterization, Radar technology

In the automotive industry, a continued strong innovation driver is the reduction of injuries and fatalities on the road. Advanced Driver Assistance Systems (ADAS) are now at the forefront of automotive innovation to prevent accidents. The ultimate goal is highly automated driving, where the vehicle takes over control, at least for some time. This requires guaranteed fail-safe operation. Guaranteed fail-safe operation in turn requires ADAS sensors and systems to be aware of their quality-of-service. Can the sensors still sense the traffic ahead, even when it snows? Do the combined sensors provide environment perception of a sufficient accuracy and reliability to define safe path and speed control strategies?

This project will identify critical determinants of radar and optical sensing reliability, will address these determinants with quality-of-service (QoS) concepts and methods both on ADAS sensor and sensor fusion level, and capture these in quality-of-service aware component interfaces and a service-based architecture, to support the development of configurable ADAS sensing systems with resultant high dependability.

Project data

Researchers: Oleg Krasnov, Rossiza Gourova
Starting date: November 2014
Closing date: November 2018
Sponsor: STW
Partners: TUD 3ME
Users: TNO, NXP
Contact: Alexander Yarovoy

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