Autonomous Driving in Traffic: Boss and the Urban Challenge

The paper “Autonomous Driving in Traffic: Boss and the Urban Challenge” by C. Urmson et al. describes DARPA’s Grand Challenges and the progress that has been made as a result of these challenges [1]. Specifically, the article focuses on the DARPA 2007 Urban Challenge and the winning car known as Boss. Boss was the first car to complete the requirements of the challenge and did so 7.5% faster than the second place finisher. This was the third challenge from DARPA focusing on autonomous driving and the first focusing on urban areas.

As part of the final round of the competition, the vehicles were required to drive 52 miles around a former military base. The military base was used to reflect a typical urban environment, yet many factors of urban environments were missing. First, only midsized cars or larger were allowed on the course, removing potential obstacles such as litter, bicycles, and pedestrians [2]. Second, the only traffic moderation devices found in the course were stop signs and the exact locations of the stop signs as well as the stop-lines were provided to the teams as highly accurate GPS waypoints. Modifications such as these severely limit the practicality to say that these vehicles are ready to be driven autonomously in an urban environment.

The winning vehicle was able to complete the challenge close to 20 minutes faster than its nearest competitor, however even the winning vehicle made 17 mistakes during the final round. Three of these errors were the result of software bugs and sensors incorrectly perceiving their surroundings. No errors occurred related to mechanical malfunctions, and the systems don’t appear to have been designed to handle such occurrences.

As hard as naive path finding is, mechanical failure such as a sensor malfunction or a flat tire while navigating at high speeds could have the potential for catastrophic failure. It will be interesting to see how autonomous cars will handle unpleasant scenarios like the aforementioned, as it can easily be assumed that current test drives only occur following rigorous maintenance and manual checking of the vehicle and its components. Further, a machine must guarantee that it is 100% error free to allow drivers to not pay attention to the road. Otherwise, drivers will still have to pay attention and spring into action when errors occur, severely constraining the benefits of autonomous vehicles.

The original article was written in 2008 and predicted deployment of autonomous haul trucks as soon as 2010. These challenges have helped to reignite the work that was started in the 1980s around autonomous vehicles, but much work is still required. Google has been working privately on their own autonomous vehicles which have collectively logged over 140,000 miles autonomously [3]. However even these autonomous cars from Google require a human to preemptively drive the path of the vehicle to map out the road conditions. All of the research in the field shows what an exciting time it is to be working on artificial intelligence and machine learning problems.

[1] Urmson, C et al. “Autonomous Driving in Traffic: Boss and the Urban Challenge”. AI Magazine. Summer 2009.
[2] Urmson, C and Whittaker, W. “Self-Driving Cars and the Urban Challenge”. IEEE Intelligent Systems. March/April 2008.
[3] Thrun, S. “What we’re driving at”. The Official Google Blog. Oct 09, 2010. http://googleblog.blogspot.com/2010/10/what-were-driving-at.html