Traffic Situation Management for Driving Automation of Articulated Heavy Road Transports - From driver behaviour towards highway autopilot
Doctoral thesis, 2017

In this thesis traffic situation management for driving automation of long combination vehicles is discussed. The automation targets high-speed driving in multiple-lane, one-way roads. Traffic situation predictions, traffic situation manoeuvres and driving principles are studied specifically. Traffic situation predictions relates to the functions used to predict how an observed traffic situation will evolve in the future. Traffic situation manoeuvres relates to decision-making regarding driving principles and control on a tactical level of driving. The developed methods and principles assume the existence of vehicle environment sensing functionalities. Furthermore, the methods have been verified using motion platform driving simulator experiments and desktop simulations. In the proposed methods for traffic situation predictions, models of the subject vehicle, driver, road and surrounding traffic have been formulated. These models capture both subject vehicle dynamics and predicted motion of surrounding traffic. Also, a unique driver steering model for articulated vehicles has been derived. Moreover, traffic situation predictions for multiple-lane one-way road driving has been derived by using driver steering and acceleration models in a closed loop with the subject vehicle model. Also, a second approach to calculate actuation trajectories has been developed and evaluated using a model predictive control framework including on-line optimisation. The derived traffic situation manoeuvres include maintain-lane, lane changes and non-evasive abort manoeuvres. It is envisaged that studying the important characteristics of manual driving will give insight into how to design driving automation especially in regard to mixed traffic with both manually driven and automated vehicles. Driving principles for driving automation are derived by using back-to-back comparisons of manual and automated driving in simulator experiments. Driving principles for initiation and execution of lane-change manoeuvres with surrounding traffic as well as managing mandatory road exits and lane changes in dense traffic have been studied and some driving principles for automation have been derived.

driver model

driving simulator

driving principles

driving automation

vehicle model

articulated heavy-vehicles

vehicle dynamics

long combination vehicles

Opponent: Dr David Cole, Engineering Department, Cambridge University, UK


Peter Nilsson

Chalmers, Applied Mechanics, Vehicle Engineering and Autonomous Systems

Driver Model Based Automated Driving of Long Vehicle Combinations in Emulated Highway Traffic

IEEE 18th International Conference on Intelligent Transportation Systems (ITSC), September 15-18, 2015, Las Palmas, Spain,; (2015)p. 361-368

Paper in proceeding

A Driver Model Using Optic Information for Longitudinal and Lateral Control of a Long Vehicle Combination

IEEE 17th International Conference on Intelligent Transportation Systems (ITSC), October 8-11, 2014. Qingdao, China,; (2014)p. 1456-1461

Paper in proceeding

A Simulator Study Comparing Characteristics of Manual and Automated Driving During Lane Changes of Long Combination Vehicles

IEEE Transactions on Intelligent Transportation Systems,; Vol. 18(2017)p. 2514-2524

Journal article

Automated highway lane changes of long vehicle combinations: A specific comparison between driver model based control and non-linear model predictive control

2015 International Symposium on Innovations in Intelligent SysTems and Applications (INISTA), September 2-4, Madrid, Spain,; (2015)p. 472-479

Paper in proceeding

P. Nilsson, L. Laine, J. Sandin, and B. Jacobson. On Actions of Long Combination Vehicle Drivers Prior To Lane Changes in Dense Highway Traffic - A Driving Simulator Study

Areas of Advance


Subject Categories

Transport Systems and Logistics

Infrastructure Engineering

Vehicle Engineering



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4311




Opponent: Dr David Cole, Engineering Department, Cambridge University, UK

More information