Injury risk curves to guide safe speed limits on Swedish roads using German crash data supplemented with estimated non-injury crashes.

Vision Zero postulates that no one should be killed or seriously injured in road traffic; therefore, it is necessary to define evidence-based speed limits to mitigate impact severity. The overall aims to guide the definition of safe speeds limits by establishing relations between impact speed and the risk of at-least-moderate (MAIS2+) and at-least-severe (MAIS3+) injuries for car occupants in frontal and side crashes in Sweden. As Swedish in-depth data are unavailable, the first objective was to assess the applicability of German In-depth Accident Study (GIDAS) data to Sweden. The second was to create unconditional injury risk curves (risk of injury given involvement in any crash), rather than risk curves conditional on the GIDAS sampling criterion of suspected-injury crashes. Thirdly, we compared the unconditional and conditional risk curves to quantify the practical implications of this methodological choice. Finally, we provide an example to demonstrate how injury risk curves facilitate the definition of safe, evidence-based speed limits in Sweden. Characteristics important for the injury outcome were similar between GIDAS and Swedish data; therefore, the injury risk curves using German GIDAS data are applicable to Sweden. The regression models yielded the following results for unconditional injury risk curves: 10 % MAIS2+ at 25 km/h impact speed for frontal head-on crashes, 20 km/h for frontal car-to-object crashes, 55 km/h in far-side crashes, and 45 km/h in near-side crashes. A 10 % MAIS3+ risk was reached between 70 and 75 km/h for all crash types. Conditional injury risk curves gave substantially different results; the 10 % MAIS3+ risk in near-side crashes was 140 km/h, twice the unconditional value. For example, if a 10 % MAIS3+ risk was acceptable, treating remaining uncertainty conservatively, assuming compliance with speed limits and that Automated Emergency Braking takes 20 km/h of the travel speed before impact in longitudinal traffic, the safe speed limit for car occupants on most Swedish roads would be 80 km/h and 60 km/h in intersections.

A model for naturalistic glance behavior around Tesla Autopilot disengagements.

OBJECTIVE: We present a model for visual behavior that can simulate the glance pattern observed around driver-initiated, non-critical disengagements of Tesla's Autopilot (AP) in naturalistic highway driving. BACKGROUND: Drivers may become inattentive when using partially-automated driving systems. The safety effects associated with inattention are unknown until we have a quantitative reference on how visual behavior changes with automation. METHODS: The model is based on glance data from 290 human initiated AP disengagement epochs. Glance duration and transition were modelled with Bayesian Generalized Linear Mixed models. RESULTS: The model replicates the observed glance pattern across drivers. The model's components show that off-road glances were longer with AP active than without and that their frequency characteristics changed. Driving-related off-road glances were less frequent with AP active than in manual driving, while non-driving related glances to the down/center-stack areas were the most frequent and the longest (22% of the glances exceeded 2 s). Little difference was found in on-road glance duration. CONCLUSION: Visual behavior patterns change before and after AP disengagement. Before disengagement, drivers looked less on road and focused more on non-driving related areas compared to after the transition to manual driving. The higher proportion of off-road glances before disengagement to manual driving were not compensated by longer glances ahead. APPLICATION: The model can be used as a reference for safety assessment or to formulate design targets for driver management systems.

Drivers anticipate lead-vehicle conflicts during automated longitudinal control: Sensory cues capture driver attention and promote appropriate and timely responses.

Adaptive Cruise Control (ACC) has been shown to reduce the exposure to critical situations by maintaining a safe speed and headway. It has also been shown that drivers adapt their visual behavior in response to the driving task demand with ACC, anticipating an impending lead vehicle conflict by directing their eyes to the forward path before a situation becomes critical. The purpose of this paper is to identify the causes related to this anticipatory mechanism, by investigating drivers' visual behavior while driving with ACC when a potential critical situation is encountered, identified as a forward collision warning (FCW) onset (including false positive warnings). This paper discusses how sensory cues capture attention to the forward path in anticipation of the FCW onset. The analysis used the naturalistic database EuroFOT to examine visual behavior with respect to two manually-coded metrics, glance location and glance eccentricity, and then related the findings to vehicle data (such as speed, acceleration, and radar information). Three sensory cues (longitudinal deceleration, looming, and brake lights) were found to be relevant for capturing driver attention and increase glances to the forward path in anticipation of the threat; the deceleration cue seems to be dominant. The results also show that the FCW acts as an effective attention-orienting mechanism when no threat anticipation is present. These findings, relevant to the study of automation, provide additional information about drivers' response to potential lead-vehicle conflicts when longitudinal control is automated. Moreover, these results suggest that sensory cues are important for alerting drivers to an impending critical situation, allowing for a prompt reaction.