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City Safety is an auto brake technology developed by Volvo Cars, designed to reduce or avoid traffic accidents.It comes in two generations, with the first operating at speeds up to 30 km/h (19 mph) and the second, functioning at speeds up to 50 km/h (31 mph).
Pedestrian detection is an essential and significant task in any intelligent video surveillance system, as it provides the fundamental information for semantic understanding of the video footages. It has an obvious extension to automotive applications due to the potential for improving safety systems. Many car manufacturers (e.g. Volvo, Ford ...
If a match is made and confirmed, the other systems in the PCAM are invoked. [5] [6] PCAM technologies can be improved with additional information from connected vehicles. [7] A thorough description of the processes for pedestrian detection in about 2010 is provided in . AI technologies have improved dramatically since then, as can be seen in ...
AEB with pedestrian detection was associated with significant reductions of 25%-27% in pedestrian crash risk and 29%-30% in pedestrian injury crash risk. However, there was not evidence that that the system was effective in dark conditions without street lighting, at speed limits of 50 mph or greater, or while the AEB- equipped vehicle was turning.
2018: Toyota released its updated Toyota Safety Sense 2.0 (TSS 2.0) to include Lane Tracing Assist, Road Sign Assist, and Low Light Pedestrian Detection with Daytime Bicyclist Detection which improves the Pre-Collision System. The first Japanese car model to receive (TSS 2.0) is the executive Crown in its 15th generation.
The pedestrian detection feature alerted the driver with an audio warning and visually enclosed the pedestrian in a box on the display which was presented via head-up display. [33] [34] The night vision system uses a separate heads up type display projected on the center bottom of the windshield. The infrared cameras do not require a light ...
An example algorithm for traffic-sign detection. Modern traffic-sign recognition systems are being developed using convolutional neural networks, mainly driven by the requirements of autonomous vehicles and self-driving cars. In these scenarios, the detection system needs to identify a variety of traffic signs and not just speed limits.
The terms "active" and "passive" are simple but important terms in the world of automotive safety. "Active safety" is used to refer to technology assisting in the prevention of a crash and "passive safety" to components of the vehicle (primarily airbags, seatbelts and the physical structure of the vehicle) that help to protect occupants during a crash.