Situational awareness , is important for recognizing scenario elements that, while not an immediate threat, may pose a hazard in the short term such as animals or turning vehicles in the periphery. As a working example we have developed a simulated driving scenario that reflects the considerations and resulting features discussed above for use in DriverLab at the Kite Research Institute . DriverLab features a 7 degrees-of-freedom motion system with a hydraulic hexapod carrying a full passenger vehicle mounted on a turntable with a 360-degree visual projection dome . Features for generating challenging conditions include a rain simulator producing real water droplets on the windshield and a glare simulator for recreating the harsh glare of oncoming headlights at night . While DriverLab is a very high-fidelity driving simulator enabling a wide range of driving situations and measures,vertical grow system the proposed framework can also be applied to driving simulators with lower levels of fidelity by selecting suitable components within the capabilities of that particular system.
The scenario contains a 24 km route with distinct terrain segments intended to be completed when participants are at or near their peak behavioural impairment due to cannabis consumption. This drive takes approximately 30 min to complete in order to provide a variety of features over the course of a reasonably natural drive, including specific scenario elements shown via icons from Table 1. Several scenario elements are innocuous or include only subtle challenges in an effort to look for a range of impairments without continual unrealistic disruptions. The scenario then transitions to a 9 km stretch of divided highway driving with significantly fewer entities, followed by a 6 km stretch of driving through the countryside with some traffic signals, vehicles, and weather changes including the onset of rain. To date, the scenario has been piloted using non-impaired drivers, demonstrating good tolerance, feasibility, and validated procedures for extracting simulated driving performance measures. In addition to pure driving metrics from the simulator , other measures can also be examined such as eye tracking , heart rate, respiration rate, expert observer ratings, and subjective ratings or questionnaires.
Here, we presented a framework for the assessment of cannabis use on driving behaviors using driving simulation technologies. The proposed framework incorporates various events and scenarios that are based on theoretical and practical considerations specifically targeting cannabis-related impairment integrated into a single driving session. To fully establish this framework, it is crucial to validate it by comparing its efficacy to other generic driving scenarios for detecting and/or characterizing cannabis-related effects, cannabis grow equipment including a careful determination of the level of sensitivity and specificity, so data collection for this purpose is the next logical step. Once validation has been successfully completed, this approach can then be applied to study more specific questions regarding cannabis-related driving impairment, such as the effects of dose, route of administration, history of use, age, and association with blood/saliva concentration levels. One key application, and the motivating factor behind the development of this framework, is quantitative determinations of cannabis-induced driving impairment to inform potential improvements with roadside impairment testing.
While the current work has focused specifically on impairment due to cannabis, interaction effects with alcohol use are also relevant and could be examined in a similar fashion as these two substances are often taken together, beginning with a consideration of the expected impairments from this combination. Upon successfully validation, the presented framework can be adopted and modified by other researchers in the field in order to study driving impairment as a result of cannabis or other drugs/medications in a targeted manner. In working through this process, simulator capabilities such as the field of view and availability of motion must be considered, as introducing some scenario features, such as hard braking, may inadvertently introduce side-effects or produce unrepresentative behaviour. In the context of regulation, a careful and detailed evaluation of the key metrics capturing driving performance deficits related to the use of cannabis could inform the development of simplified roadside testing tools for authorities to determine driving impairment more accurately. This would entail following the procedure set forth in this text, beginning with the identified acute effects of cannabis use or the identification of acute effects from usage of the drug under investigation. Next, the safe operating abilities within the desired field must be determined to establish independent test measures and dependent variables that can capture hypothesized deficits in performance.