Truck steering systems, modeling, analysis and driver support

Trucks are overrepresented in accident statistics and many truck accidents occur in merging scenarios. Simulation studies explored the potential of car and truck merging support systems but actual sensors and human machine interfaces have not been implemented. This project will develop a merging assistant for a DAF tractor-trailer combination.

PhD Candidate: ir. Jan Loof
Supervisor: dr. ir. Igo Besselink,
Promoter: prof. dr. Henk Nijmeijer
Support: STW
Project Period: September 2013 - August 2017

Due to complex vehicle dynamics, size, and limited driver vision, trucks are overrepresented in accident statistics. A wide range of truck driver support systems is currently being introduced and a future prospect is autonomous highway driving where the truck operator is a supervisor rather than an active controller of the vehicle. However, before autonomous highway driving becomes feasible, two challenges need to be addressed. First, vehicle dynamics of trucks need to be better understood, and second, human factors aspects need to be incorporated in automation design.

This project will develop a series of innovations that represent a step toward autonomous driving. Our focus is on a specific safety-critical highway task: merging. Using wheel load measurements, steering system measurements, and RADAR we will obtain a precise understanding of the lateral merging motion of modern trucks. Using this information, a merging assistant will be developed, a system that will provide adaptive levels of support varying from providing information to enhance driver situation awareness, towards guidance and automation. Human machine interfaces will optimally combine haptic (force), acoustic and visual cues. The system will be experimentally evaluated in a driving simulator, with specific focus on evaluating safety and human factors side effects such as behavioral adaptation.