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Future cars as mobile supercomputers

12 Oct 2015  | Klaus Neuenhuskes

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Cars that can "see" have moved successfully from film fantasy into real life in many of today's high-end and mid-range road cars. Car manufacturers are keen to continue exploring the technology, and safety legislators may soon require any or all vehicles approved for sale to be fitted with certain active safety systems that can bet realised by implementing automotive image recognition systems. This pattern is already established in e.g. the EU, where safety systems such as ABS that were once considered high-end accessories are now mandatory in all vehicles.

Engineers developing vision-based safety systems such as pedestrian detection and collision avoidance now need to consider whether the design decisions they take today will allow them to deliver solutions suitable for mass-market applications in the near future. Some chipmakers targeting this market are already beginning to paint exciting images of tomorrow's cars as mobile supercomputers, leveraging technologies such as intensive multi-core architectures as currently used in high-performance PC-graphics cards and gaming platforms. However, important factors such as cost and power consumption in particular may demand a more focused approach.

In addition to supporting various types of hazard recognition, visual recognition is expected to complement collision avoidance technologies such as radar and ultrasonic, sometimes even by replacing them. Additional potential safety applications include systems to eliminate blind spots or to replace conventional mirrors for better rearward visibility. Ultimately, vision-based systems will play a major role in autonomous cars that are capable of navigating safely to a destination and parking without human intervention to respond to road conditions, observe traffic signs and avoid numerous types of hazards.

The industry's vision for vision suggests that in-car systems will become increasingly complex, with more and more cameras and screens around and inside the vehicle. On-board image-processing capability will need to scale accordingly, while continuing to deliver reliable real-time performance, with acceptablely low latency.

Image processing: Hardware or software
Historically, image recognition has been largely reliant on software algorithms running on one or more digital signal processors (DSPs). As more and more video channels need to be handled simultaneously, the demand on processing resources increases. In a desktop computing system, this capability can be satisfied by increasing the operating frequency of the graphics processor. In an automotive application, however, the power dissipated by a high-performance CPU/DSP array running at high-megahertz frequencies would present unacceptable thermal challenges.

For space reasons, a significant proportion of the system electronics must be mounted inside ECUs with narrow space constraints. These are typically mounted behind the windscreen and often exposed to direct sunlight. It is therefore important to ensure real-time system performance without resorting to power-hungry architecture that dissipate large amounts of heat.

An image recognition processor architecture featuring dedicated hardware blocks for complex or frequently-used processing functions such as transforms, filters, histograms and pyramid matching can provide a platform for more energy-efficient processing generating less heat while also benefiting from faster algorithm execution resulting in consistent real-time performance. This type of platform can also scale linearly to support extra ADAS functions or additional channels for multiple cameras.

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