Automotive sensor detects pending collisions

Edit Hook

"Life-like object detection for collision avoidance using spatiotemporal image processing." (from project website)

Biomimicry Story

"These systems will be based on the outstanding performance of natural visual sensory-processing systems. As a central reference point, the project focuses on the integrated visual neuro-system for collision avoidance found in grasshoppers, which will be studied, modelled and emulated by means of sensory-processing chips in standard CMOS technologies. The resulting electronic systems will be able to operate correctly within the wide range of environmental conditions encountered in real-life automotive applications, and will meet the strict reliability standards of the car industry." (from project website)

Challenges Solved

"Collision threat detection and avoidance defines a major Research and Development challenge for the automotive industry. Adaptive cruise-control systems incorporating some collision-avoidance features are offered today as pricey options on luxury cars. However, the performance of these systems is not always sufficient, and their cost is too high for wide use. Significant improvements are still needed for these systems to perform satisfactory and to become popular. Future cruise-control systems will probably fuse data from different types of sensors, with optical images, and therefore vision, playing a significant role. However, present conventional approaches to vision, consisting of a camera that acquires the data and a separate digital processor that process it, are too slow for the most demanding tasks. New solutions are needed. This project brings together a multi-disciplinary team including mixed-signal microelectronic designers (IMSE-CNM), one company in the automotive sector (VCC), neurobiologists (UNEW), and experts on opto-electronic information technology and hardware/software systems (ANCL-HAS) to target the development of these new solutions. The project embraces advanced research and development activities focused on creating single-chip bio-inspired visual perception systems for automotive applications." (from project website)

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