Driver and Pedestrian Mutual Awareness for Path Prediction and Collision Risk Estimation

We present a novel method for vehicle-pedestrian path prediction that takes into account the awareness of the driver and the pedestrian towards each other. The method jointly models the paths of vehicle and pedestrian within a single Dynamic Bayesian Network (DBN). In this DBN, sub-graphs model the environment and entity-specific context cues...

Prediction-Based Reachability Analysis for Collision Risk Assessment on Highways

Real-time safety systems are crucial components of intelligent vehicles. This paper introduces a prediction-based collision risk assessment approach on highways. Given a point mass vehicle dynamics system, a stochastic forward reachable set considering two-dimensional motion with vehicle state probability distributions is firstly established. We...

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...