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Pedestrian crash avoidance mitigation (PCAM) systems (USDOT Volpe Center [1]), also known as pedestrian protection or detection systems, use computer and artificial intelligence technology to recognize pedestrians and bicycles in an automobile's path to take action for safety.
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 ...
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).
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.
2013: Volvo introduced the first cyclist detection system. All Volvo automobiles now come standard with a lidar laser sensor that monitors the front of the roadway, and if a potential collision is detected, the safety belts will retract to reduce excess slack. Volvo now includes this safety device as an option in FH series trucks.
2008 update added pedestrian detection system on the redesigned BMW 7 Series (F01), which flashes a caution symbol on the navigation/information screen and automotive head-up display when it detects pedestrians. [26] 2013 update added Dynamic Light Spot. 2013 update added animal detection. The system provides a real-time video image that also ...
The sequence of events in a car-pedestrian crash. Many pedestrian crashes involve a forward moving car (as opposed to buses and other vehicles with a vertical hood/bonnet). In such a crash, a standing or walking pedestrian is struck and accelerated to the speed of the car and then continues forward as the car brakes to a halt.
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.