Effect of Delays/Lag and Fighting it in Self-driving Neural Networks
Name
Ilmar Uduste
Abstract
Autonomous driving is a field of interest for academia and industry alike, with hopes of fully replacing humans in the driver seat with artificial intelligence. Recent advances in the domain have been made in the use of end-to-end self-driving models as opposed to modular approaches.
However, problem with building these end-to-end pipelines is that delays (lag) are commonly not taken into account. This work investigates the effect of lag in self-driving nets using Donkey Car, an autonomous car platform, and finds that the car drives worse when there is more lag in the pipeline. A novel method called frameshift is proposed to fight against the lag present and models using frameshift are proven to have significant real-life (closed-loop) performance gains over the baseline self-driving net, even in no-lag conditions and when comparing models analytically (open-loop). Frameshift is shown to be an effective tool in fighting against lag, although performance in very lag-heavy environments is inconsistent, as the car makes too few decisions per second to have consistent behaviour.
The findings presented show the need to test self-driving cars in real-life (closed-loop) as opposed to just analytically (open-loop) and also open up the field of end-to-end self-driving nets to the concept of using frameshift as a potential tool to fight against lag, although this novel idea requires further testing in different conditions and real-world data, to come to a final conclusion.
However, problem with building these end-to-end pipelines is that delays (lag) are commonly not taken into account. This work investigates the effect of lag in self-driving nets using Donkey Car, an autonomous car platform, and finds that the car drives worse when there is more lag in the pipeline. A novel method called frameshift is proposed to fight against the lag present and models using frameshift are proven to have significant real-life (closed-loop) performance gains over the baseline self-driving net, even in no-lag conditions and when comparing models analytically (open-loop). Frameshift is shown to be an effective tool in fighting against lag, although performance in very lag-heavy environments is inconsistent, as the car makes too few decisions per second to have consistent behaviour.
The findings presented show the need to test self-driving cars in real-life (closed-loop) as opposed to just analytically (open-loop) and also open up the field of end-to-end self-driving nets to the concept of using frameshift as a potential tool to fight against lag, although this novel idea requires further testing in different conditions and real-world data, to come to a final conclusion.
Graduation Thesis language
English
Graduation Thesis type
Master - Data Science
Supervisor(s)
Ardi Tampuu
Defence year
2022