Real-Time Predictive Modeling and Robust Avoidance of Pedestrians with Uncertain, Changing Intentions
This addresses safety challenges for autonomous vehicles in urban environments, though it appears incremental as it builds on existing motion prediction and planning methods.
The paper tackles the problem of predicting pedestrian intentions for autonomous vehicles by developing a changepoint detection and clustering algorithm combined with a Gaussian process mixture model, which improves prediction accuracy and enables real-time, probabilistically safe trajectory planning.
To plan safe trajectories in urban environments, autonomous vehicles must be able to quickly assess the future intentions of dynamic agents. Pedestrians are particularly challenging to model, as their motion patterns are often uncertain and/or unknown a priori. This paper presents a novel changepoint detection and clustering algorithm that, when coupled with offline unsupervised learning of a Gaussian process mixture model (DPGP), enables quick detection of changes in intent and online learning of motion patterns not seen in prior training data. The resulting long-term movement predictions demonstrate improved accuracy relative to offline learning alone, in terms of both intent and trajectory prediction. By embedding these predictions within a chance-constrained motion planner, trajectories which are probabilistically safe to pedestrian motions can be identified in real-time. Hardware experiments demonstrate that this approach can accurately predict pedestrian motion patterns from onboard sensor/perception data and facilitate robust navigation within a dynamic environment.