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Safety continues to be one of the key concerns in automotive design. In addition to keeping drivers and passengers safe, automotive manufacturers are looking for ways to protect people outside the car—such as bicyclists and pedestrians. While airbags and seat belts are designed to provide protection inside the vehicle, accident avoidance equipment, through applications such as vision-based warning systems, could provide the ultimate solution for protecting lives both inside and out.
Analysis shows that the vast majority of accidents are caused by human error or misjudgment. Automotive electronics that could monitor driving conditions and warn a driver at the onset of a hazardous situation could go a long way in preventing and mitigating human-induced accidents.
When vision-based warning systems for drivers were first introduced, acceptance from consumers was limited, mainly due to high costs and a lack of reliability related to false warnings and such. Systems have improved dramatically in recent years, and new vision systems are now being introduced by companies such as Toyota for its flagship Lexus LS460 (below). Current technologies enable automotive designers to implement robust and powerful recognition algorithms while meeting the stringent power consumption and operation constraints associated with automotive design.
The challenges—performance and power consumption
One key consideration when developing a vision system for cars is the need for rapid processing of camera images. For example, a car moving at 75 miles per hour would travel about 110 ft/second. Common video cameras capture images at a rate of 30 frames/second, which means the car would travel 3.6 ft between the time an image was captured and the time it became available. To recognize hazards within 7 ft at this speed, a vision system would have to be able to analyze images before the next image was captured.
Such performance does not come easily. In the Lexus LS460, the system analyzes images of lane markers, nearby objects, and pedestrians that are captured by two stereo cameras.
In an automotive system using a conventional microprocessor or signal processor, the dedicated hardware filters preprocess the incoming image before the main processor can do its analysis, as the microprocessor or signal processor does not deliver the required performance (see Figure 1 (left)).
Figure 1: In a conventional system (left), time-consuming preprocessing needs to be done and hardware-based logic hampers changes or upgrades. A software-based, parallel processing architecture, such as IMAPCAR, facilitates processing speed and changes (right).
In addition to requiring dedicated hardware logic for filtering, a conventional system also limits the possibility of adding simple algorithm upgrades if a change in the hardware filter is necessary, or of implementing new features if a system's pre-filters conflict with the filters for the existing features.
NEC Electronics developed a solution, now called IMAPCAR®, that enables system developers to implement all filters and recognition algorithms in a high-level software language, eliminating the need for hardware filtering (see Figure 1 (right)). The key challenges included not only achieving performance high enough to do even complex filtering in software, but also keeping power consumption for the device below 2W. Any power consumption above 2W usually requires dedicated heat-loss countermeasures when operating under the automotive temperature range (-40C to +85C). This impacts not only system costs, but also limits the options for mounting the system in a car.
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