Evaluating rider steering responses to an unexpected collision hazard using a motorcycle riding simulator

•Realistic emergency steer responses can be safely explored in a simulator.•Swerving is 8× more successful when time to collision is 1.5 rather than 1 s.•Mean response time of 570 ms was independent of time to collision or practice.•With a 1.5 s time window steering is more accurate and improves with practice.•Higher steer response variability characterizes failure, not enough time to respond. Motorcycle rider steer responses to unexpected collision emergencies have not been studied experimentally. We used a motorcycle simulator with elastic steer mechanism and modified car driving model to simulate the input-output counter steering response of two-wheeled vehicles in combination with a car pop-up paradigm from driving studies to evaluate rider responses to unexpected collision hazards. We manipulated time-to-crash – either 1 s or 1.5 s – to probe the threshold between not enough and just enough time to respond. The median response time of 570 ms was similar to previously recorded latencies for motorcycle emergency braking responses. Although median response time and steer torque did not depend on time-to-crash (TTC), the distribution and variability of response measures were increased when TTC was shorter. Riders showed improved lateral displacement toward the road centerline by the third TTC1.5 trial and were almost 8 times more likely to produce a successful virtual swerve avoidance maneuver in TTC1.5 rather than TTC1 trials. With 1.5 s to respond, riders were more consistent, with net steering inputs more congruent with the maneuvering goal compared to when they had only 1 s to respond. Comparisons of ‘safe’ versus ‘crash’ outcomes show that average response times across trials and riders are not different, but the variance in timing is lower across successful trials. We have shown than it is possible to safely study rider reactions to an emergency and observe a realistic range of steer responses to a traffic conflict using a motorcycle riding simulator. The results have relevance for the design of automatic rider assistive systems, rider behavior prediction and decision logic algorithms, PTW rider modeling and targeted training interventions.