Machine Learning-based Lane-level Localization

Abstract

In autonomous driving, for the vehicle to make a decision on its own it has to have precise knowledge of its location (lane) with respect to its environment. This problem of determining the lane on which the vehicle is travelling is called Lane-Level Localization (LLL). HD maps with localization algorithms are used to solve the problem of Lane-Level Localization. However, during the initialization phase, there is uncertainty in the confidence of the lane in which the vehicle is driving. Our thesis aims to overcome this problem by using the Multi-Hypothesis Tracking (MHT) approach. Multi-Hypothesis Tracking has two parts which are tracking multiple hypotheses and inference of the correct hypothesis by eliminating the wrong hypotheses. Our thesis focuses on the latter part which can be solved using the early classification of time series technique. The early classification of time series technique tends to make an early and accurate classification of the hypothesis by rejecting wrong hypotheses based on the class probabilities assigned to different hypotheses and using multi-objective optimization to find a trade-off between earliness and accuracy. Our model produced an accuracy of 99.053% with an earliness of 0.109.