Leave the Driving to It
How would lives and landscapes change if every car had a computer in the driver’s seat?
Are We Almost There Yet?
The car that will take Jane to the city and then go park itself will not be a 2012 model. The car that can reduce highway fatalities by 99 percent will not be a 2012 model either. Measured against those goals, the current state of the art for robotic vehicles looks pretty wimpy—but not hopeless.
Here’s a story that gives a hint of what computer-controlled driving is like today. A vehicle named Talos, built by a team at MIT, was being prepared for the DARPA Urban Challenge, a competition staged in 2007 by the Defense Advanced Research Projects Agency. In preliminary testing, Talos was nearing an intersection when it noticed another vehicle approaching on the cross street. The other car had the right of way, and so Talos stopped to wait. But then, because of “sensor noise,” Talos momentarily lost track of the car. When the sensors reacquired the signal, the planning and guidance logic concluded it was seeing a new vehicle, which had just arrived at the intersection. This meant that Talos had priority, and so it started forward. So did the other car. They didn’t collide, but it was a near thing.
This incident offers a fascinating glimpse into the mind of an alien intelligence, trying to make sense of a world where cars can pop in and out of existence without warning. The story also suggests we are still a long way from creating a computational agent with the kind of common sense needed to pass the road test for a driver’s license. I believe this assessment of the situation is correct, but it gives the wrong impression about the prospects for building driverless automobiles.
Much of the recent work on autonomous vehicles treats driving as a problem in artificial intelligence and computer vision. The challenge is to extract meaning from sensory signals and form an accurate conceptual model of the roadway situation. With great effort, this approach may eventually succeed, though perhaps only in creating a computer driver as fallible as a human one.
There’s another way to go about it. Instead of trying to replicate the driver’s sensory faculties and mental model of the world, we can reengineer the world itself so that sophisticated perception and cognition are no longer needed. Consider again two cars at an intersection, trying to decide who goes first. Human drivers rely on subtle forms of communication, including eye contact and occasional hand-waving, to resolve this situation. Perhaps a computer could learn to do the same thing, but an easier course is to provide a data channel over which the cars can communicate and negotiate directly.
Aviation offers a useful point of reference. Commercial aircraft routinely fly under computer control (except at takeoff and landing). But the aircraft autopilot does not look out the window and try to interpret visual cues. Instead, the flight-management system relies on ground-based beacons, satellite signals and inertial navigation, as well as plane-to-plane data links for collision avoidance. Meanwhile a central facility (air-traffic control) coordinates the movements of aircraft and resolves conflicts.
Admittedly, navigation and traffic management are easier in the wide-open spaces of the sky than on crowded, quasi-one-dimensional roadways, but the same principles could be applied. With appropriate infrastructure, each car could have accurate and timely information about the state of the roadway and nearby cars—their positions, velocities and intentions. Even with such complete information, computing optimal paths for all the cars remains formidably difficult, but it is a problem of algorithms and control theory, not cognitive science.
The key first step in making this approach feasible is building a communication network linking nearby vehicles and roadside relay stations. Standards for such networks are already in preparation. Some version of the network is likely to be implemented soon for less-grandiose purposes, such as traffic reporting and entertainment.
Building new infrastructure is slow and expensive, so even if all the technological problems were solved, it would be years before large numbers of cars on large numbers of roads were routinely taking charge of their own movements. Thus there’s time for planning and choosing what kind of transportation system we’d like to have. It’s a good moment to be asking not just “Can we get there from here?” but also “Do we want to go?”
- Broggi, A. 1999. Automatic Vehicle Guidance: The Experience of the ARGO Autonomous Vehicle. River Edge, N.J.: World Scientific.
- Campbell, M., et al. 2010. Autonomous driving in urban environments: approaches, lessons and challenges. Philosophical Transactions of the Royal Society 368:4649–4672.
- Chester, M., A. Horvath and S. Madanat. 2010. Parking infrastructure: energy, emissions, and automobile life-cycle environmental accounting. Environmental Research Letters 5:034001.
- Fletcher, L., et al. 2008. The MIT–Cornell collision and why it happened. Journal of Field Robotics 25:775–807.
- Folsom, T. C. 2011. Social ramifications of autonomous urban land vehicles. IEEE International Symposium on Technology and Society, May 2011, Chicago. http://www.enviroteach.com/social_ramifications.pdf.
- Furda, A., and L. Vlacic. 2011. Enabling safe autonomous driving in real-world city traffic using multiple criteria decision making. IEEE Intelligent Transportation Systems Magazine 3(1):4–17.
- Hartenstein, H., and K. P. Laberteaux. 2010. VANET: Vehicular Applications and Inter-Networking Technologies. Chichester, U.K.: John Wiley and Sons.
- Kalra, N., J. Anderson and M. Wachs. 2009. Liability and regulation of autonomous vehicle technologies. California Partners for Advanced Transit and Highways Research Report UCB-ITS-PRR-2009-28.
- McCarthy, N. 2009. Autonomous Systems: Social, Legal and Ethical Issues. London: Royal Academy of Engineering. http://www.raeng.org.uk/autonomoussystems.
- Ni, D., et al. 2010. Preliminary estimate of highway capacity benefit attainable with IntelliDrive technologies. In Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, pp. 819–824.
- Schakel, W. J., B. van Arem and B. D. Netten. 2010. Effects of cooperative adaptive cruise control on traffic flow stability. In Proceedings of the 13th International IEEE Conference on Intelligent Transportation Systems, pp. 759–764.
- Shladover, S. E. 2009. Cooperative (rather than autonomous) vehicle-highway automation systems. IEEE Intelligent Transportation Systems Magazine 1(1):10–19.
- Shoup, D. 2007. Cruising for parking. Access: The Magazine of the University of California Transportation Center 30:16–22.
- Thrun, S. 2010. Toward robotic cars. Communications of the ACM 53(4):99–106.
» Post Comment