Driver behavior models for evaluating automotive active safety: From neural dynamics to vehicle dynamics
Doktorsavhandling, 2015

The main topic of this thesis is how to realistically model driver behavior in computer simulations of safety critical traffic events, an increasingly important tool for evaluating automotive active safety systems. By means of a comprehensive literature review, it was found that current driver models are generally poorly validated on relevant near-crash behavior data. Furthermore, competing models have often not been compared to one another in actual simulation. An applied example, concerning heavy truck electronic stability control (ESC) on low-friction road surfaces (anti-skidding support), is used to illustrate the benefits of simulation-based system evaluation with a driver model, verified to reproduce human behavior. First, a data collection experiment was carried out in a moving-base driving simulator. Then, as a complement to conventional statistical analysis, a number of driver models were fitted to the observed steering behavior, and compared to one another. The best-fitting model was implemented in closed-loop simulation. This approach permitted the conclusion that heavy truck ESC provides a safety benefit in unexpected critical maneuvering, something which has not been previously demonstrated. Furthermore, ESC impact could be analyzed at the level of individual steering behaviors and scenarios, and this impact was found to range from negligible, when the simulated drivers managed well without the system, to large, when they did not. In severe skidding, ESC reduced maximum body slip in the simulations by 73 %, on average. Some specific ideas for improvements to the ESC system were identified as well. As a secondary applied example, an advanced emergency brake system (AEBS) is considered, and a partially novel approach is sketched for its evaluation in what-if resimulation of actual recorded crashes. A number of new insights and hypotheses regarding driver behavior in near-crash situations are presented: When stabilizing a skidding vehicle, drivers were found to employ a rather simple and seemingly suboptimal yaw rate nulling strategy. Collision avoidance steering was found to be best described as an open-loop steering pulse of constant duration, regardless of amplitude. Furthermore, by analysis of data from test tracks as well as real-life crashes and near-crashes, it was found that detection of a collision threat, and also the timing of driver braking or steering in response to it, may be affected by a combination of situation kinematics and processes of neural evidence accumulation. These ideas have been tied together into a modeling framework, describing driving control in general as constructed from intermittent, ballistic control adjustments. These, in turn, are based on overlearned sensorimotor heuristics, which allow near-optimal, vehicle-adapted performance in routine driving, but which may deteriorate into suboptimality in rarely experienced situations such as near-crashes.


active safety

control behavior

system evaluation

Driver models

Virtual Development Laboratory, entrance at Hörsalsvägen 7A, Chalmers University of Technology
Opponent: Prof. Erwin Boer, Department of Biomechanical Engineering, Delft University of Technology, The Netherlands


Gustav M Markkula

Chalmers, Tillämpad mekanik, Fordonsteknik och autonoma system

Modeling driver control behavior in both routine and near-accident driving

Proceedings of the Human Factors and Ergonomics Society,; Vol. 58(2014)p. 879-883

Paper i proceeding

Driver behaviour in unexpected critical events and in repeated exposures – a comparison

European Transport Research Review,; Vol. 6(2014)p. 51-60

Artikel i vetenskaplig tidskrift

Effects of experience and electronic stability control on low friction collision avoidance in a truck driving simulator

Accident Analysis and Prevention,; Vol. 50(2013)p. 1266-1277

Artikel i vetenskaplig tidskrift

Comparing and validating models of driver steering behaviour in collision avoidance and vehicle stabilisation

Vehicle System Dynamics,; Vol. 52(2014)p. 1658-1680

Artikel i vetenskaplig tidskrift

A Review of Near-Collision Driver Behavior Models

Human Factors,; Vol. 54(2012)p. 1117-1143

Artikel i vetenskaplig tidskrift

De undermedvetna knep som en förare använder för att med perfekt resultat hantera vardagens trafiksituationer, kan vara otillräckliga i situationer där det finns risk för en olycka. Detta är ett generellt budskap från denna doktorsavhandling, där matematiska modeller av förarbeteende har utvecklats, som ett slags kognitiva krockdockor att användas i datorsimuleringar av kritiska trafiksituationer. Sådana datorsimuleringar används bland annat av fordonstillverkare för att testa och förbättra fordonens säkerhetssystem, tidigare oftast med föraren beskriven som slumpmässig i sitt beteende, eller utifrån en ingenjörsmässig tanke om föraren som en regulator, med exakt förståelse av trafiksituationen och sitt fordon. Här har det påvisats att man bättre kan beskriva faktiskt observerat förarbeteende i kritiska situationer om man istället anammar ett psykologiskt/neurovetenskapligt perspektiv på föraren, och antar att bilkörning är något vi till stor del klarar av tack vare noggrant intrimmade, men ändå ungefärliga tumregler och knep, som fungerar bra nästan jämt. När bilen framför bromsar hårt kan samma tumregler dock till exempel leda till att vi själva bromsar för lite, och när vi får sladd kan våra vanliga knep för att följa vägen få oss att överkompensera i vår styrning. Extra kraftfulla blir dessa modeller när de kombineras med ingenjörsmetoder för att beskriva hur förarbeteendet samverkar med fordonet.

The subconscious tricks that drivers use to manage everyday driving with perfect results, can be inappropriate in situations with risk of accident. That is one general message from this doctoral thesis, in which mathematical models of driver behavior have been developed, as cognitive crash test dummies of sorts, for use in computer simulations of critical traffic situations. Such simulations are used by for example vehicle manufacturers, to test and improve their vehicles’ safety systems. Previously, near-crash driver behavior has generally been simulated as either random, or from an engineer’s perspective of the driver as a controller, with a precise grasp of the traffic situation and the vehicle. Here, it has been shown that actual observed behavior in critical situations can be better described if one instead adopts a psychological/neuroscientific perspective, and assumes that driving is something that we achieve thanks to carefully adjusted, but nevertheless approximate, heuristics and tricks that work beautifully almost always. However, when the driver in front slams the brakes, the same heuristics can for example cause us to brake too little, and during skidding our everyday tricks for following the road can lead to steering overcompensations. This type of driver model becomes especially useful when combined with engineering methods for describing how the driver behavior interacts with the vehicle.




Tillämpad psykologi




Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie

Virtual Development Laboratory, entrance at Hörsalsvägen 7A, Chalmers University of Technology

Opponent: Prof. Erwin Boer, Department of Biomechanical Engineering, Delft University of Technology, The Netherlands

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