A Low-Cost Lane-Following Algorithm for Cyber-Physical Robots
Not on the map. Why?
Abstract (arXiv)
Duckiebots are low-cost mobile robots that are widely used in the fields of research and education. Although there are existing self-driving algorithms for the Duckietown platform, they are either too complex or perform too poorly to navigate a multi-lane track. Moreover, it is essential to give memory and computational resources to a Duckiebot so it can perform additional tasks such as out-of-distribution input detection. In order to satisfy these constraints, we built a low-cost autonomous driving algorithm capable of driving on a two-lane track. The algorithm uses traditional computer vision techniques to identify the central lane on the track and obtain the relevant steering angle. The steering is then controlled by a PID controller that smoothens the movement of the Duckiebot. The performance of the algorithm was compared to that of the NeurIPS 2018 AI Driving Olympics (AIDO) finalists, and it outperformed all but one finalists. The two main contributions of our algorithm are its low computational requirements and very quick set-up, with ongoing efforts to make it more reliable.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 6 of its 9 references that are in the map. First posted on arXiv 23 Aug 2022.
Builds on
6 of 9 references.
References · 6
- 2017Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real Worldcited by 1,401
- 2016End to End Learning for Self-Driving Carscited by 1,000
- 2018Towards End-to-End Lane Detection: an Instance Segmentation Approachcited by 129
- 2019The AI Driving Olympics at NeurIPS 2018cited by 6
- 2021Embedded out-of-distribution detection on an autonomous robot platformcited by 6
- 2020Sim-to-real reinforcement learning applied to end-to-end vehicle controlcited by 4
Built on by
Not cited yet.