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Embedded vision enhances driver-assistance designs

Posted: 04 Feb 2013     Print Version  Bookmark and Share

Keywords:Forward facing Advanced Driver Assistance Systems  Embedded Vision Alliance  OpenCL 

Forward Facing Advanced Driver Assistance Systems (FF ADAS) are becoming more and more important, especially as the European New Car Assessment Programme (Euro NCAP) ratings are tied to Autonomous Emergency Braking (AEB) and other important car safety features. Euro NCAP recently announced a new rating scheme for the years 2013 to 2017, which is also driving ADAS trends for players across the automotive market, from tier 1s and manufacturers, to semiconductor suppliers.

ADAS applications such as AEB and Lane Departure Warning (LDW) will be taken much more strongly into account by 2014, and will be key for achieving a top Euro NCAP 5-star rating, which significantly impacts car manufacturers' sales rates. Along with AEB and LDW, other features such as Intelligent Highbeam Control (IHC) and Road Sign Detection (RSD) are now integrated on most standard FF ADAS cameras. Low- to mid-range systems of the near future will possess at least some version of AEB (described below), LDW and IHC.

It is evident that the auto industry is under pressure to find ways to implement these highly advanced, computationally and network intensive systems without driving up the bill-of-materials (BOM) per vehicle. Indeed, the introduction of these new applications will likely evolve as an overall cost reduction in the ADAS camera platform. Ideally, automakers will want to have a common 'smart' platform from which they can introduce and deploy systems differentiated by feature sets across their various vehicle classes. This means a single platform design for a smart FF ADAS processing camera that is low power, has low BOM cost, and is scalable across vehicle classes, especially low to mid-range.

Application challenges
There is a host of design challenges for a FF ADAS processing camera targeted at the applications described above. System design requirements which shape the final solution include:

 • High-definition (HD) imaging and multi-function support: Applications like people detection, and especially road sign detection, drive higher resolution image sensor requirements. Existing implementations are VGA but are rapidly migrating to megapixel and beyond in next generation systems. In addition with multiple functions, there is a requirement for the system to scale and partition image frames real time in advance of algorithmic processing. Along with increasing image sensor resolution, expect higher frame rates (for example, up to 60fps) to support functions like pedestrian detection at higher vehicular speeds (for example, 60km/h for NCAP testing).
 • Computational support: Vision processing requires a careful mapping of software to the embedded hardware to meet demand for very large computational requirements.
 • Data flow: Depending on the implementation, there is likely a requirement to support high bandwidth data movement especially between sensor interface, system memory and processor core.
 • Differentiation: Tier-1's and OEM'S must differentiate and run non-standard analytics algorithms. This requires open, programmable systems that support easy porting of their in-house algorithm code base.
 • Temperature sensitivity: Smart camera systems are traditionally situated against the windshield in front of the rear view mirror. This is one of a vehicle's more challenging thermal environments, due to direct sun and environmental exposure. Systems, therefore, must be temperature sensitive.
 • Power sensitivity: The thermal requirements mentioned above necessitate a tight control of power dissipation. This is particularly true given the sensitivity of higher resolution image sensors to thermal noise.
 • Low cost: Cost is a major factor in many markets. Due to the cost-sensitive nature of FF ADAS, manufacturers expect silicon optimised to provide maximum features at appropriate price ranges and within compact chip form factors. Devices with cores that are specific to programmable vision processing at low power and low die area are typically more competitive.
 • Functional safety: Vision-based ADAS applications are moving beyond providing information to the driver through useful LEDs to life-critical active safety systems. FF ADAS processing cameras will be subject to the same safety considerations that have existed with radar systems (for example, for ACC).

Vision algorithm development
It becomes evident from the above that the best solutions will be low power, cost-optimised and high performance, both in terms of managed high bandwidth data streams and sophisticated algorithmic processing itself. However, in most cases algorithm development for ADAS applications is initially developed on a PC and often based on non-optimised algorithms found in OpenCV or provided in Matlab.

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