Darpa urban challenge 2007 -- the last great robot project

 Before the year 2010, artificial intelligence was mostly a niche discipline within computer science without any impact to society. The reason was, that most of the projects were in an early stage and lots of technical obstacle were visible. Because of this limitation its interesting from a science history perspective to take a closer look what the self understanding was of AI in the past.

The goal of the Darpa urban challenge 2097 was to program self driving cars for an urban environment. These normal size cars were able to stop at a junction and do some parking maneuvers. According to the large amount of documentation and some of the talks from the teams its possible to extract some general principle how the cars were realized. Building safe driving cars in the past was recognized as a hardware and software challenge. One problem was to squeeze high performance server racks into the car's trunk. A second and more serious problem was to write all the software.

One team has written software with 100k lines of code, the next one has even created a software with 500k lines of code. The idea in 2007 was to treat self driving car software similar to a large scale software project similar to the linux kernel. Therfor the existing toolchain was used, namely a C/C++ compiler and modern version control systems including bug trackers. The logic of the car was encoded in endless amount of path planning algorithms, C++ classes and dedicated particle filter for self-localization.

It should be mentioned that the outcome of these large scale projects was poor. Despite the fact that a team of experienced programmers have written all the code, the resulting autonomous car was unable to navigate on the street. Simple task like waypoint following was successful demonstrated, but more complex problems like a road block and unexpected situation have overwhelmed the car's AI software.

On the one hand, the shown self driving cars were more powerful than every attempt in the past to build such vehicles. At the same time it was obvious that these cars were not ready for real world traffic. One disappointed detail was that all the written C/C++ software was only working for the original car but can*t be adapted to other cars or another sort of robotics vehicle. In technical terms, the software wasn't scaling up to slightly different problems which is a sign of bad software design.

In 2007 it was unclear how to write better software which fits to the needs of self driving cars. The reason was a certain bias about the project which was: a) the car is designed as an autonomous vehicle b) the decision making process is implemented in software and planning algorithms. So it was a autonomous computational vehicle which is from a modern AI perspective a dead end. In 2007 nobody was able to see the limitation of these constraints but it was imagined, that AI has to be realized this way.

Let us go a step backward and describe the motivation for the darpa urban challenge. The self understanding in 2007 about robotics was, that robotics is a hardware and software problem and located within computer science. The goal was to make sure that the hardware of a self driving car is working, which means that the lidar is rotating fast enough and that the powerful server build into the car gets enough electricity. The second goal was to program the software which means to utilize the C/C++ and implement powerful algorithms in a robot control system. The hope was that the combination of hardware and software would enable a robot car to take its own decision.