A bioinspired collision detection algorithm for VLSI implementation

This source preferred by Rick Stafford

Authors: Cuadri, J., Linan, G., Stafford, R., Keil, M.S. and Roca, E.

Editors: Carmona, R.A. and LinanCembrano, G.

Pages: 238-248

DOI: 10.1117/12.607837

This data was imported from Scopus:

Authors: Cuadri, J., Liñ́n, G., Stafford, R., Keil, M.S. and Roca, E.

Volume: 5839

Pages: 238-248

DOI: 10.1117/12.607837

In this paper a bioinspired algorithm for collision detection is proposed, based on previous models of the locust (Locusta migratoria) visual system reported by F.C. Rind and her group, in the University of Newcastle-upon- Tyne1,2. The algorithm is suitable for VLSI implementation in standard CMOS technologies as a system-on-chip for automotive applications. The working principle of the algorithm is to process a video stream that represents the current scenario, and to fire an alarm whenever an object approaches on a collision course. Moreover, it establishes a scale of warning states, from no danger to collision alarm, depending on the activity detected in the current scenario. In the worst case, the minimum time before collision at which the model fires the collision alarm is 40 msec (1 frame before, at 25 frames per second). Since the average time to successfully fire an airbag system is 2 msec, even in the worst case, this algorithm would be very helpful to more efficiently arm the airbag system, or even take some kind of collision avoidance countermeasures. Furthermore, two additional modules have been included: a "Topological Feature Estimator" and an "Attention Focusing Algorithm". The former takes into account the shape of the approaching object to decide whether it is a person, a road line or a car. This helps to take more adequate countermeasures and to filter false alarms. The latter centres the processing power into the most active zones of the input frame, thus saving memory and processing time resources.

This data was imported from Web of Science (Lite):

Authors: Cuadri, J., Linan, G., Stafford, R., Keil, M.S. and Roca, E.

Volume: 5839

Pages: 238-248

DOI: 10.1117/12.607837

The data on this page was last updated at 04:42 on June 23, 2017.