Small-scale cars for autonomous driving research

Abstract

Self-driving cars (autonomous cars, robo cars) are vehicles that can drive without human input. In the recent decades, this field of science has received a lot of attention. There is a growing need to improve the safety and effectiveness of traffic. Self-driving cars have the promise of being safe. The main reasoning is that by removing human error, and utilising algorithms, the number of mistakes and accidents that would arise in most situations could be drastically reduced. Autonomous driving is a heavily investigated area currently, but significant challenges remain open, thus requiring significant research during the decades to come. As the cost of life-size test platforms for autonomous-driving research is very high, the need to use smaller-scale vehicles to be employed as test platforms arises. This thesis investigates the viability of such a method, by evaluating the performance and suitability of a small-scale self-driving car platform, the Donkey Car, for different aspects of autonomous driving research. Perception, localization and mapping, planning and end-to-end capabilities of the platform were investigated and compared with publicly available real-life self-driving car experiments. The results achieved showed that small-scale cars are capable of performing autonomous driving research at an acceptable level in comparison with real-life cars.