Context-based Cyclist Path Prediction: Crafted and Learned Models for Intelligent Vehicles

This thesis addresses the problem of path prediction for cyclists.<br/>Instead of solely focusing on how to predict the future trajectory based on previous position measurements, this thesis investigates how to leverage additional contextual information that can inform on the future intent of cyclists.<br/>This thesis does this with the...

Crafted vs. Learned Representations in Predictive Models - A Case Study on Cyclist Path Prediction

This paper compares two models for context-based path prediction of objects with switching dynamics: a Dynamic Bayesian Network (DBN) and a Recurrent Neural Network (RNN). These models are instances of two larger model categories, distinguished by whether expert knowledge is explicitly crafted into the state representation (and thus is...

Context-based path prediction for targets with switching dynamics

Anticipating future situations from streaming sensor data is a key perception challenge for mobile robotics and automated vehicles. We address the problem of predicting the path of objects with multiple dynamic modes. The dynamics of such targets can be described by a Switching Linear Dynamical System (SLDS). However, predictions from this...