A safety score for the assessment of driving style.

OBJECTIVE: Road traffic laws explicitly refer to a safe and cautious driving style as a means of ensuring safety. For automated vehicles to adhere to these laws, objective measurements of safe and cautious behavior in normal driving conditions are required. This paper describes the conception, implementation and initial testing of an objective scoring system that assigns safety indexes to observed driving style, and aggregates them to provide an overall safety score for a given driving session. METHODS: The safety score was developed by matching safety indexes with maneuver-based parameter ranges processed from an existing highway traffic data set with a newly developed algorithm. The concept stands on the idea that safety, rather than suddenly changing from a safe to an unsafe condition at a certain parameter value, can be better modeled as a continuum of values that consider the safety margins available for interactions among multiple vehicles and that depend on present traffic conditions. A sensitivity test of the developed safety score was conducted by comparing the results of applying the algorithm to two drivers in a simulator who were instructed to drive normally and risky, respectively. RESULTS: The evaluation of normal driving statistics provided suitable ranges for safety parameters like vehicle distances, time headways, and time to collision based on real traffic data. The sensitivity test provided preliminary evidence that the scoring method can discriminate between safe and risky drivers based on their driving style. In contrast to previous approaches, collision situations are not needed for this assessment. CONCLUSIONS: The developed safety score shows potential for assessing the level of safety of automated vehicle (AV) behavior in traffic, including AV ability to avoid exposure to collision-prone situations. Occasional bad scores may occur even for good drivers or autonomously driving vehicles. However, if the safety index becomes low during a significant part of a driving session, due to frequent or harsh safety margin violations, the corresponding driving style should not be accepted for driving in real traffic.

Can We Study Autonomous Driving Comfort in Moving-Base Driving Simulators? A Validation Study.

OBJECTIVE: To lay the basis of studying autonomous driving comfort using driving simulators, we assessed the behavioral validity of two moving-base simulator configurations by contrasting them with a test-track setting. BACKGROUND: With increasing level of automation, driving comfort becomes increasingly important. Simulators provide a safe environment to study perceived comfort in autonomous driving. To date, however, no studies were conducted in relation to comfort in autonomous driving to determine the extent to which results from simulator studies can be transferred to on-road driving conditions. METHOD: Participants ( N = 72) experienced six differently parameterized lane-change and deceleration maneuvers and subsequently rated the comfort of each scenario. One group of participants experienced the maneuvers on a test-track setting, whereas two other groups experienced them in one of two moving-base simulator configurations. RESULTS: We could demonstrate relative and absolute validity for one of the two simulator configurations. Subsequent analyses revealed that the validity of the simulator highly depends on the parameterization of the motion system. CONCLUSION: Moving-base simulation can be a useful research tool to study driving comfort in autonomous vehicles. However, our results point at a preference for subunity scaling factors for both lateral and longitudinal motion cues, which might be explained by an underestimation of speed in virtual environments. APPLICATION: In line with previous studies, we recommend lateral- and longitudinal-motion scaling factors of approximately 50% to 60% in order to obtain valid results for both active and passive driving tasks.