[Technology Report]
Four-Wheeled Supercomputers
Automotive safety equipment turns vehicles into the largest collection of mobile computing and sensing equipment you'll own.
Smart phones. MP3 players. Notebooks. We can’t live without our portable gadgets. However, we probably drive our most computeintensive mobile electronics to work everyday. Today’s automobiles use a variety of networks, sensors, and computer platforms to deliver safer and more pleasant travel than ever.
Most companies concentrate their development efforts on safety, efficiency, and performance. These features rank high with consumers, and the newest and most sophisticated features always appear first in high-end models like the Lexus LS 460 (Fig. 1).
As with all modern cars, the LS 460 offers mandatory passive safety features such as seatbelts and airbags. It also has a voice-activated, heads-down-display (HDD) navigation system that’s standard in many high-end vehicles and an option in most others. The LS 460 leads the pack by moving into the active safety realm with NEC’s IMAPCAR (Image Memory Array Processor for CAR) image-recognition system.
MOVE TO ACTIVE SAFETY Passive safety systems are mature technologies that have less payback for new improvements, but they continue to be refined. They target post-crash actions, meaning they activate after a collision has occurred or is inevitable (e.g., airbags).
Active safety features address accident avoidance or pre-crash actions. Advanced antilock braking, traction control, and vehicle stability controls also fall into this arena. Traction control and vehicle stability controls benefit from improved sensors as well as the significantly greater computing capabilities that are available in the latest crop of DSPs and microcontrollers.
These active systems contribute to improved safety and performance. But designers also are addressing new areas due to improvements and cost reductions in sensors, such as video cameras, lasers, and radar detectors. From a driver’s standpoint, new systems like adaptive cruise control are an active part of the driving process. They augment the driver’s senses and provide limited autonomous control. In the future, cars will exercise more autonomous control.
For example, initial cruise-control applications simply maintained a fixed speed. Some current systems can maintain a safe but variable distance from the cars ahead based on the environment. Even more advanced systems, like those on the LS 460, can apply the brakes in anticipation of a collision. Warnings are being improved as well, from simple tones to more complex audio and visual cues.
New systems warn the driver if there’s a high probability of a collision. If the driver doesn’t react in time, the system will employ recommended actions such as braking. Passive systems also assist in anticipating the collision and operating in a more optimal fashion. For instance, airbags needn’t be deployed when no one is sitting in their respective seats, or they can deploy with less force if the occupant is small. Weight sensors help make these determinations, but ultrasonic or even vision systems can be employed, too.
Today’s designs incorporate more sensors to provide more contextual, environmental information so computers can become part of the decision loop (Fig. 2). Sensor fusion, or the combination of sensor information for a typical task, will become more common. Adaptive cruise control can use vision and radar sensors to determine where an obstacle, such as another vehicle, is located. No one sensor system meets all of the requirements for current and forthcoming active safety systems, but vision is definitely one of them.
ON THE HORIZON Low-cost, high-performance imaging and computational hardware is bringing vision to the forefront of automotive safety. So are improved algorithms and applications for image recognition and analysis. Yet the availability of this kind of hardware in versions suitable for automotive use will be critical to their widespread adoption. Eventually, vision systems will be required by law, just like seatbelts and airbags.
Multicore architectures that have very large numbers of processing units will continue to grow. The current NEC IMAPCAR processor employs 128 very long instruction word (VLIW) processing elements (PEs) (Fig. 3). Each VLIW instruction can control four logic units in each PE. A 16-bit RISC control unit provides the coordination for the IMAPCAR chip (Fig. 4).
The IMAPCAR’s architecture is designed specifically for video-feedback applications in the automotive market. It incorporates the video input and output into the buffering scheme for real-time annotation.
The system can handle a number of imagerecognition algorithms at the same time, providing information to the host microcontroller as well as to the driver by modifying the video stream as it passes through the chip. Each PE contains its own memory for copying and analyzing the frame buffer as necessary. Error correction coding (ECC) and parity are used to improve reliability.
Low power is also critical to this application space. The IMAPCAR chip draws only 1.7 W running at 100 MHz, delivering 100 GOPS of performance. Currently, the IMAPCAR system can handle lane and pedestrian recognition. The Lexus system employs two cameras in a stereo configuration as well as millimeter-wave radar, delivering features such as lane departure warnings. A third camera covers rear viewing.
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