Introduction
C-ITS systems that warn the motorcycle rider of an upcoming danger only work if the rider is interpreting the warning correctly and reacts accordingly. So far however, there is little knowledge about how long a rider reaction towards a warning takes.
Additionally, the question arises whether reactions from the passenger car domain can be applied to PTWs.
The following whitepapers describe two dynamic motorcycle riding simulator studies by CMC, which investigated motorcycle riders’ reaction times towards different types of warnings. Such knowledge can bridge the gap between results from the accidentology side to the use case and test case specific strategies. The latter focus on the decision on how an application’s display/ alert principle should be designed (e.g., advisory notification, crash warning, active intervention).
Additionally, the question arises whether reactions from the passenger car domain can be applied to PTWs.
The following whitepapers describe two dynamic motorcycle riding simulator studies by CMC, which investigated motorcycle riders’ reaction times towards different types of warnings. Such knowledge can bridge the gap between results from the accidentology side to the use case and test case specific strategies. The latter focus on the decision on how an application’s display/ alert principle should be designed (e.g., advisory notification, crash warning, active intervention).
What has been investigated
CMC has published two subsequent whitepapers regarding Rider Reaction Time based on a dynamic motorcycle riding simulator study.
The RRT I whitepaper dates from 2022 and remains available on this page for reference.
The RRT II whitepaper was written by the end of 2023 as an extension to RRT I, so that it contains all information on both studies.
In RRT I, the focus was on the effect of a generic visual warning in the dashboard.
In RRT II, other types of warnings were included:
Reactions in an urban and a rural scenario were tested. These did not include imminent crash warnings, but advisory warnings with 3 seconds between warning onset and the potentially critical situation becoming visible. A baseline measurement was included which investigated rider responses in the same scenarios without any warning.
These studies are a first step towards empirical evidence in this domain.
The RRT I whitepaper dates from 2022 and remains available on this page for reference.
The RRT II whitepaper was written by the end of 2023 as an extension to RRT I, so that it contains all information on both studies.
In RRT I, the focus was on the effect of a generic visual warning in the dashboard.
In RRT II, other types of warnings were included:
- Visual: mirror-mounted LEDs
- Visual: Head-Up Display
- Auditory: warning tone
- Haptic: vibration pattern of a wrist band
Reactions in an urban and a rural scenario were tested. These did not include imminent crash warnings, but advisory warnings with 3 seconds between warning onset and the potentially critical situation becoming visible. A baseline measurement was included which investigated rider responses in the same scenarios without any warning.
These studies are a first step towards empirical evidence in this domain.
Important outcomes
RRT I:
- In 16.7% of cases, the purely visual dashboard warning was not recognized at all.
- Among the other cases, the average time between onset of the notification and gaze towards the dashboard was already about 1 second.
- The average time between notification onset and ‘throttle off’ was about 2 seconds.
- The average time between notification onset and ‘initiate braking’ was about 2.5 seconds.
- The mentioned reaction times were shorter in the urban scenario compared to the rural one, in which the situation was perceived as less critical.
- All four investigated warning types were superior to the baseline condition.
- Mirror-mounted LEDs and the haptic bracelet had no missed warnings at all.
- PTW-fixed devices such as the mirror-mounted LEDs had the highest acceptance due to reasons of comfort (no additional device to take care of) and safety (no stable connection between PTW and external device necessary).
- The primarily reported response across all types of warnings was an attention allocation to the forward roadway.
- The earlier attention allocation allows for less respectively later decelerations.
Other findings
Another interesting observation could be that, in the more time-critical urban scenario, all riders who had seen the warning, initiated braking before the obstacle became visible. In combination with the favourable evaluation of the test riders after the experiment, this shows a good potential for the safety benefit of C-ITS applications.
In comparison to driver reaction times in passenger car studies, more missed warnings were observed for some of the warning types, reaction times seem longer and reaction time distributions seem wider; hence there is a clear need for PTW-specific reaction time studies.
Furthermore, RRT II showed the potential of different types of warnings in terms of rider reactions as well as subjective measures such as acceptance. These studies’ results can contribute to rider safety e.g., by means of an improved understanding of user requirements regarding different types of warnings and regarding the timing of notifications; Additionally, by means of delivering valuable input to rider behaviour models in the context of simulation.
In comparison to driver reaction times in passenger car studies, more missed warnings were observed for some of the warning types, reaction times seem longer and reaction time distributions seem wider; hence there is a clear need for PTW-specific reaction time studies.
Furthermore, RRT II showed the potential of different types of warnings in terms of rider reactions as well as subjective measures such as acceptance. These studies’ results can contribute to rider safety e.g., by means of an improved understanding of user requirements regarding different types of warnings and regarding the timing of notifications; Additionally, by means of delivering valuable input to rider behaviour models in the context of simulation.