A Non-Parametric Unsupervised Approach for Robust Lane-Change Estimation From Highway-Based Trajectory Data

A review of lane change estimation algorithms shows they typically rely on trajectory data with high-definition maps or camera-based data, both of which are costly. Departing from these approaches, our work introduces an innovative method that extracts lane changes solely from highway GPS data, eliminating the need for lane-level maps or camera input. We treat each trajectory as a combination of road curvatures which correspond to large scale variations, and lane changes and lane deviations which correspond to small scale variations. We project each trajectory into linear basis functions at different window sizes, aggregating the deviations for each point on the trajectory. We refer to this transformed space as basis-aligned coordinate system (BACS). Leveraging these BACS features, we propose two methods for extracting lane changes: (a) using an inverse operation on the transformed curves along with an optimization procedure to detect deviations, and (b) applying consistency measures across window sizes to identify anomalous regions as lane changes. We show that our approach can be theoretically justified and can robustly recover lane changes while simultaneously controlling for random noise. We utilize real trajectories collected in the Safety Pilot Model Deployment project and show the performance of our proposed approach through precision-recall curves when compared to two different ground truths based on maps and camera data respectively. We also provide ablation studies regarding the choice of window sizes, highlighting how the projection distances become smoother as a function of window size.