The purpose of this study was to establish and validate a driving simulator method for assessing drug effects on driving. To achieve this, we used ethanol as a positive control, and examined whether ethanol affects driving performance in the simulator, and whether these effects are consistent with performance during real driving on a test track, also under the influence of ethanol. Twenty healthy male volunteers underwent a total of six driving trials of 1h duration; three in an instrumented vehicle on a closed-circuit test track that closely resembled rural Norwegian road conditions, and three in the simulator with a driving scenario modelled after the test track. Test subjects were either sober or titrated to blood alcohol concentration (BAC) levels of 0.5g/L and 0.9g/L. The study was conducted in a randomised, cross-over, single-blind fashion, using placebo drinks and placebo pills as confounders. The primary outcome measure was standard deviation of lateral position (SDLP; "weaving"). Eighteen test subjects completed all six driving trials, and complete data were acquired from 18 subjects in the simulator and 10 subjects on the test track, respectively. There was a positive dose-response relationship between higher ethanol concentrations and increases in SDLP in both the simulator and on the test track (p<0.001 for both). In the simulator, this dose-response was evident already after 15min of driving. SDLP values were higher and showed a larger inter-individual variability in the simulator than on the test track. Most subjects displayed a similar relationship between BAC and SDLP in the simulator and on the test track; however, a few subjects showed striking dissimilarities, with very high SDLP values in the simulator. This may reflect the lack of perceived danger in the simulator, causing reckless driving in a few test subjects. Overall, the results suggest that SDLP in the driving simulator is a sensitive measure of ethanol impaired driving. The comparison with real driving implies relative external validity of the simulator.

The main finding in this simulator study was that codeine does not impair driving-related abilities over and above what is associated with chronic pain per se.

No abstract

One purpose of Intelligent Vehicles is to improve road safety, throughput, and emissions. However, the predicted effects are not always as large as aimed for. Part of this is due to indirect behavioral changes of drivers, also called behavioral adaptation. Behavioral adaptation (BA) refers to unintended behavior that arises following a change to the road traffic system. Qualitative models of behavioral adaptation (formerly known as risk compensation) describe BA by the change in the subjectively perceived enhancement of the safety margins. If a driver thinks that the system is able to enhance safety and also perceives the change in behavior as advantageous, adaptation occurs. The amount of adaptation is (indirectly) influenced by the driver personality and trust in the system. This also means that the amount of adaptation differs between user groups and even within one driver or changes over time.Examples of behavioral change are the generation of extra mobility (e.g., taking the car instead of the train), road use by ''less qualified'' drivers (e.g., novice drivers), driving under more difficult conditions (e.g., driving on slippery roads), or a change in distance to the vehicle ahead (e.g., driving closer to a lead vehicle with ABS).In effect predictions, behavioral adaptation should be taken into account. Even though it may reduce beneficial effects, BA (normally) does not eliminate the positive effects. How much the effects are reduced depends on the type of ADAS, the design of the ADAS, the driver, the current state of the driver, and the local traffic and weather conditions.