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...

Hierarchical Optimal Maneuver Planning and Trajectory Control at On-Ramps With Multiple Mainstream Lanes

Connected Automated Vehicles (CAVs) have the potential to improve traffic operations when they cooperatively maneuver in merging sections. State-of-the-art approaches in cooperative merging either build on heuristics solutions or prohibit mainline CAVs to change lane on multilane highways. This paper proposes a hierarchical cooperative...

Distributed Model-Free Adaptive Predictive Control for Urban Traffic Networks

Data-driven control without using mathematical models is a promising research direction for urban traffic control due to the massive amounts of traffic data generated every day. This article proposes a novel distributed model-free adaptive predictive control (D-MFAPC) approach for multiregion urban traffic networks. More specifically, the...

Curve Tilting With Nonlinear Model Predictive Control for Enhancing Motion Comfort

The benefits of automated driving can only be fully realized if the occupants are protected from motion sickness. Active suspensions hold the potential to raise the comfort level in automated passenger vehicles by enabling new functionalities in chassis control. One example is to actively lean the vehicle body toward the center of the corner...

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...

Using road topology to improve cyclist path prediction

We learn motion models for cyclist path prediction on real-world tracks obtained from a moving vehicle, and propose to exploit the local road topology to obtain better predictive distributions. The tracks are extracted from the Tsinghua-Daimler Cyclist Benchmark for cyclist detection, and corrected for vehicle egomotion. Tracks are then...