Automobile sensors may usher in self-driving cars
Margery Conner, Technical Editor - May 26, 2011
Google last year demonstrated the results
of its research-and-development efforts
to create an autonomous vehicle. The
small fleet of specially equipped cars—six Toyota Priuses and one Audi TT—has logged more than 140,000 miles
of daytime and nighttime driving in
California, including traversing San
Francisco’s famously crooked Lombard
Street and the Los Angeles freeways (Figure 1). In all cases,
an engineer was in the driver’s seat, monitoring each car’s
performance and ready to take over if necessary.A robocar of the future would be so intelligent that its driver would be able to read, play, or work rather than piloting the car. The benefits would include safety, freeing up the driver for other tasks or recreation, and the more effective use of the traffic infrastructure due to more efficient traffic regulation and fuel efficiency.
Motor-vehicle accidents are the leading cause of death of 13- to 29-year-olds in the United States. According to Sebastian Thrun, an engineer at Google and the director of the Stanford Artificial Intelligence Laboratory, which created the Google robocar, almost all of these accidents are the result of human error rather than machine error, and he believes that machines can prevent some of these accidents.
“We could change the capacity of
highways by a factor of two or three
if we didn’t rely on human precision
for staying in the lane and [instead]
depended on robotic precision,” says
Thrun. “[We could] thereby drive a
little bit closer together in a little bit
narrower lanes and do away with all
traffic jams on highways.”Doubling highway capacity by a factor of three with no added infrastructure costs and freeing an hour or two a day for productive or relaxing pursuits seem like worthy goals, but how close is the auto industry to achieving a practical self-driving car? Google is not in the car-production business and has no business plan for monetizing its research (Reference 1). In Google’s approach, autonomous vehicles will not require a government mandate to become reality. The Google fleet uses LIDAR (light-detection-and-ranging) technology, such as that in a system available from Velodyne’s HDL (high-definition LIDAR)-64D laser-sensor system, which uses 64 spinning lasers and then gathers 1.3 million points/sec to create a virtual model of its surroundings. One reason to use LIDAR rather than radar is that the laser’s high-erenergy, shorter-wavelength laser light better reflects nonmetallic surfaces, such as humans and wooden power poles. Google combines the LIDAR system with vision cameras and algorithmic vision-processing systems to construct and react to a 3-D view of the world through which it is driving (Reference 2).
The enabling sensor hardware in the vehicles enables the cars to see everything around them and make decisions about every aspect of driving, according to Thrun. Although we are not close yet to a fully autonomous vehicle, the technology, including the sensor platform of radar, ultrasonic sensors, and cameras, is available in today’s intelligent vehicle. It remains only to standardize the car’s hardware platform and develop the software. Cars are approaching the point that smartphone platforms had reached just before the introduction of the Apple iPhone and the Motorola Android.
As sensors decrease in price and increase in integration, they will become ubiquitous in all cars. Once users accept them as normal parts of a car, then automotive-OEM companies can integrate more intelligence into them until they achieve the goal of an autonomous car. Today’s intelligent automobile can perform many driver-assistance tasks, such as avoiding and preventing accidents and reducing the severity of accidents. To perform these tasks, the vehicles have passive safety systems, such as air bags and seat belts; active safety systems, such as electronic stability control, adaptive suspension, and yaw and roll control; and driver-assistance systems, including adaptive cruise control, blind-spot detection, lane-departure warning, drowsy-driver alert, and parking assistance. These systems require many of the same sensors that the autonomous car requires: ultrasonic sensors, radar, LIDAR systems, and vision-imaging cameras.
Cars now use ultrasonic sensors to provide proximity detection for low-speed events, such as parallel parking and low-speed collision avoidance. Ultrasonic detection works only at low speeds because it senses acoustic waves; when the car is moving faster than a person can walk, the ultrasonic sensor is blind.
Although ultrasonic-sensor technology is more mature and less expensive than radar, car designers who care about the aesthetics of the car’s appearance are reluctant to have too many sensor apertures visible on the car’s exterior. As a more powerful and more flexible technology, radar should begin to replace ultrasonic sensors in future designs (Figure 2).
Most automotive radar systems currently are not highly integrated, taking up significant space, and are costly. Analog Devices’ recently introduced AD8283 integrated automotive-radar-receiver analog front end represents the increasing integration that decreases the size and cost of automotive radar (Figure 3). It will sell for about 50% less than a discrete design for an automotive analog front end and fits into a 10×10-mm package. “The market is moving toward putting radar into nonluxury vehicles—cars for the rest of us,” says Sam Weinstein, product manager for the Precision Linear Group at Analog Devices. The sample price for a six-channel AD8283 is $12.44 (1000).
Out-of-position sensing similarly uses IR cameras. Today’s passenger seats must have pressure sensors to determine the weight of the passenger and use the information to deploy the passenger’s air bags. The air bags deploy at different speeds, depending on the weight of the passenger. This sensor does not know, however, whether the passenger is leaning on the dashboard, reclining in the seat, or moving to the left or the right. The closer the passenger is to the deploying air bag, the greater the impact. The camera monitors the passenger’s position, and, upon impact, deploys the air bag appropriately to the passenger’s size and position.
These cameras use IR LEDs rather than those in the visible spectrum because they must be able to work at night. It would be distracting to illuminate the driver or the passenger with visible light for the camera to sense. The human eye detects light as visible at distances as great as approximately 700 nm, whereas IR cameras detect 850- to 900-nm-distant light.
IR imaging also has a place outside the car for crash avoidance, and these applications require IR illumination. According to Sevugan Nagappan, marketing manager of the infrared business unit at Osram Opto Semiconductors, IR cameras can help in collision avoidance by seeing beyond what the high beams illuminate. “IR-LED illumination allows you to see when you can’t have your high beams on to see past your headlamps, for example, allowing the system to see beyond the headlights to see and avoid a deer entering the road,” he says.
IR LEDs’ primary use has so far been
in remote controls. However, these
inexpensive LEDs use 10 mW or less of
power. Automotive applications require
output power of greater than 1W to
illuminate the subject. In addition, the
IR LED must be small enough to fit next
to the IR camera and be inconspicuous.
Nagappan estimates that the camera
needs to measure less than 10 mm2,
and illuminating the IR LED requires
5 mm2. He says that manufacturers can
make LEDs in small packages that can
provide 3.5W and that these devices
are enabling new applications. Osram’s
3.5W SFH 4236 IR LED has an integral
lens with a narrow beam angle to focus
the IR light, increase the beam intensity,
and focus the beam into the eye box
to watch the driver’s eyes.Innovation is also driving down the
cost of the cameras. The Fraunhofer
Institute expects to bring to market a
camera as small as a grain of salt and
costing only a few euros. The resolution
currently is 250×250 pixels. These
cameras could replace side-view mirrors,
reducing airflow drag (Figure 4).
You can reach Technical Editor Margery Conner at 1-805-461-8242 and margery.conner@ubm.com.
| References |
|
| For More Information | ||||
| Analog Devices | Apple Computer | BMW | Ford | Fraunhofer Institute |
| Google | Infiniti | Mercedes-Benz | Motorola | Osram Opto
Semiconductors |
| Stanford Artificial
Intelligence
Laboratory | Toyota | Velodyne |
Editor's note: The original version of this article contained an error, which has been corrected in Figure 3 above and in the associated PDF file. "AFE: Analog front end" was changed to "AAF: Anti-aliasing filter" on May 26, 2011.
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