Carolo cup as the last great adventure in AI

 Since 2008 there is a programming challange available with autonomous model cars. Its a smaller version of the DARPA urban challenge which took place in 2007. So both competitions were started before the year 2010 and they are providing interesting insigth into the self-understanding of robotics projects in this area.

The overall idea behind the Carolo cup was, that its some sort of C/C++ microncontroller programming challenge. The team has to built the assemble the hardware, program the software and make sure that the car gets the maximum score in the competition. Typical problems available are the SLAM problem which is self-localisation on a map, prediction of the future with the model predictive control module and creating a sensor model. All these subtasks are realized with the C/C++ programming language because its the standard for microcontroller programming and a great choice for algorithm implementation.

What makes the Darpa urban challenge in 2007 and the carolo cup since 2008 remarkable is, that all the possible problems are solved by increasing the amount of code lines. The idea is, that a robot car is some sort of Linux kernel which has to growth in size to fit to demanding problems. The consequence is, that creating the AI for the robot means basically to write more source code in C++, bugfix the code and iterate this cycle all the time.

For the Darpa urban challenge, the average team was using 150k lines of code written in C. The source code for Carolo cup participants is available at github and contains also of endless amount of code. There are self written 3d simulators, path planners and sensor perception API available. The amount of effort to create all these C++ code is huge.

From a historic perspective, the software development cycle used for maintaining the Carolo cup source code can be seen as the last classical approach in AI. until 2010 the dominant paradigm in robotics was, to treat the project as a computer science project which consists of hardware and software frameworks. it was based on microcontrollers, path planners, and endless amount of highly efficient computer code. At the same time, the result of this effort was low. Even for simple tasks like moving in a circle, the written C++ code isn't working. Its not possible to reuse the program for real cars and it will take decades until the problems are fixed.

Until 2010 the focos on hard computer science topics like a certain programming challenge and a certain microcontroller was a common bias in artificial intelligence. Possible alternatives like machine learning were rejected as too complicated.