KTM AG, a global frontrunner in two-wheeler innovation, is pushing forward into the domain of artificial intelligence and deep learning, and we're inviting you to be a part of this groundbreaking journey. Introducing the KTM AG inaugural Code Challenge, an exciting 3-month online journey designed to harness the collective intelligence, ignite passion, and translate visionary ideas into transformative two-wheeler technology. 

Tasks Description: At the heart of this challenge, participants are tasked with developing an algorithm for a high-beam lighting system that uses a pixel matrix. The primary goal is to:

This creates an adaptive high-beam system that targets and dims only specific areas aligned with detected objects, ensuring the rest of the road remains well-lit. Further guidelines are available in the Dataset subsection.

The Competion Structure:

The datathon unfolds in a 3-tiered cascade model:

Level 1
Participants are provided with a video filmed in "easy" conditions, and with a number of "Key Frames" which show the ideal output of the algorithm at these snapshots in time. The illumination state of the pixels at that point in time are encoded into a CSV file. The algorithm of the participant should aim to output the pixels as close as possible to what is requested in the key frame. This algorithm is then assessed by an automatic assessment, and then a manual, on board assessment, to verify the code will run on the target hardware. A score is provided on how accurate the participants' key frames match those of KTM AG and if the minimum score is reached the candidate may pass to Level 2.

Level 2
Participants are provided with a new video in more difficult conditions along with another set of Key Frames.The assessment method is similar to Level 1, except the tolerance on the Key Frames will be tighter and the minimum score to pass to Level 3 will be higher.

Level 3
Participants are provided with another new video, with further difficult conditions and the corresponding Key Frames. The first two assessment phases are similar to that of Level 1 and Level 2, and the quality level will be similar to that of Level 2.  Unlike Level 2, the efficiency and processing footprint of the algorithm will also be analysed and the submission with the highest quality and smallest footprint is eligible to win to the final prize.