Motorcycle Safety and Advanced Rider Assistance Systems (ARAS)February 2023 • Sources:
IntroductionAdvanced driver assistance systems (ADAS), such as adaptive cruise control (ACC), automatic emergency braking (AEB), and blind-spot monitoring (BSM) for passenger vehicles are becoming ubiquitous, with some features being standard on vehicles.1 The same has not been true for motorcycles. However, analogous advanced rider assistance systems (ARAS) have been introduced, and the ARAS market is expected to grow significantly in the coming years.2 ARAS are equipment that support and assist the motorcycle operator and may also reduce stress and strain; ARAS are intended as means of supporting accident mitigation and may help reduce harmful energy involved during pre-crash phases.3 Questions of interest for the current article are: why might there be a lag in ARAS implementation, and what implications might the answers have for incident examinations and claims? Passenger Vehicles and MotorcyclesTo address these questions, it helps to first acknowledge pertinent differences between passenger vehicles and motorcycles. Passenger vehicles are typically manufactured with passenger restraint systems, whereas most motorcycles are not. Most passenger vehicles are steered by a driver using a steering wheel, whereas motorcycles achieve maneuvering through a combination of counter-steering and a rider and motorcycle leaning in intended directions of travel, especially at higher speeds. Motorcycles tend to have more pronounced movements of pitch (forward/backward), roll (lean left/right), and yaw (clockwise/counterclockwise) than passenger vehicles.4 The body of a rider is also typically more involved in achieving these vehicle dynamics.5 Though not exhaustive, these observations highlight some engineering and human factors differences that may present challenges for the implementation of ARAS. Engineering and Human Factors ChallengesA notable engineering challenge concerns radar-based detection of objects on the road. Greater dynamic pitch, roll, and yaw movements can constrain radar projections, and the effectiveness of radar can be reduced when the motorcycle is leaned and during other riding phases due to vibrations.6,7 For these reasons and others, ARAS implementation by motorcycle manufacturers, at this point, has been limited mainly to ACC and BSM.8 It should be noted, however, that development of helmet-based ARAS technologies has made significant progress; the main goals of these “smart helmet” features are to provide riders with blind spot, rear, and front collision indications via head-up visual displays and/or auditory alerts. 1 Akamatsu et al., 2013; IIHS, 2016 Given the interest in advanced motorcycle safety technologies, a more in-depth review of human factors challenges for ARAS has recently been provided.9 One general issue is that ARAS may produce unique or unexpected riding situations that may impact rider performance.10 As Coelho et al. (in press) point out, riders keep themselves aboard, at least in part, by grasping the handlebars, bracing the motorcycle with their legs, and keeping their center of mass in the appropriate position. Combined with the fact that there are usually no passenger restraints, these observations mean that abrupt unexpected changes in the orientation and/or dynamics of the motorcycle, such as through application of motorcycle AEB, could result in control problems, rider separation from the motorcycle, or both. Thus, pertinent issues include the degrees to which the ARAS technology can predict and/or detect the state of the rider and the degrees to which the rider can predict and/or detect and respond to the assistive actions on the motorcycle. Other more specific scientific issues involved with ARAS include operator acceptability, trust, attention, warnings perception, and learning. One promising feature concerns the ratio of sight distance (i.e., how far away the operator is looking along the path of travel) to stopping distance (i.e., operator perception-reaction time plus braking distance). This ratio has been identified as a critical variable for motorcycle operator safety because operators can override their sight distance, especially when attention drifts.11 When attention drifts, operators may not attend to upcoming curves or hazards in front of them. ARAS such as CW systems may help bring attention back to the task, and CW systems have been shown to help some operators adapt to curves earlier than other riders.12 ARAS holds promise for not only alerting operators to such overriding in the moment but also for helping them better understand and predict the limitations of their vehicles and of themselves. Indeed, the potential for ARAS to assist the operator in on-road hazard detection and reduction of instances of sight-distance overriding is an exciting advance in assistive motorcycle technologies. A likely path for this advance, at least initially, is through effective multimodal ARAS warnings; this suggestion is supported by evidence that such warnings may be helpful in car and motorcycle assistive technology contexts.13 Implications for Examinations and ClaimsWith the proliferation of ADAS there has been an increase in claims and lawsuits alleging either that unequipped vehicles should have been or that vehicles equipped with ADAS should have performed differently. There have also been allegations that aspects of the human interaction with ADAS technology may have contributed to the incident. Allegations can center on false or unintentional activation of ADAS features and on problems with a driver responding to signals from the technology. Areas of inquiry in these matters are often whether and how the system provided assistance when the driver did not intend or expect it to and whether and how the system became engaged or disengaged with or without the driver noticing. Other questions involve whether a manufacturer should have provided ADAS that functioned in a specific way or with specific warning or intervention timing or how ADAS indications are presented in terms of sensory modality, such as through vision, audition, or touch, and in terms of physical characteristics, such as frequency, duration, and intensity. 9 Coelho et al., in press There is no reason to believe that this situation will be any different in ARAS-related incidents. And there may well be important nuances of assistive technologies for motorcycle operators that render the above complaints unique. For instance, more pronounced vehicle dynamics of motorcycles combined with more extensive involvement of the rider in those dynamics may have implications for allegations that an ARAS feature presented a challenging transition from a situation where the assistive system was operating to one where a rider may have needed to respond. Addressing this issue will undoubtedly require additional complex analyses of human performance and vehicle dynamics issues, such as rider attention to the state of the motorcycle and perceptual-motor controllability, as well as whether and how the ARAS is capable of monitoring or predicting the state of the rider and of the vehicle at the same time. Closing RemarksIn conclusion, there seems to be an emerging consensus that widespread implementation and adoption of ARAS is not a matter of “if” but a matter of “when.” Moreover, the engineering and scientific questions will likely be similar to some of the ADAS-related questions that have arisen, but will also likely be somewhat novel due to unique characteristics of motorcycling. The unique issues of ARAS incident examination will certainly call for thorough understandings of human-machine interaction and motorcycle vehicle dynamics. 14 Kuschefski et al., 2010; Pieve et al., 2009 ReferencesAkamatsu, M., Green, P., & Bengler, K. (2013). Automotive technology and human factors research: Past, present, and future. International Journal of Vehicular Technology, 1-28. Coelho, C. J., Garets, S. B., Bailey, J. D., Frank, T. A., Scully, I. D., & Cades, D. M. (in press). Human factors issues of advanced rider assistance systems. 14th International Conference on Applied Human Factors and Ergonomics, San Francisco, CA, United States. Diederichs, F., Knauss, A., Wilbrink, M., Lilis, Y., Chrysochoou, E., Anund, A., Bekiaris, E., Nikolaou, S., Finér, S., Zanovello, L., Maroudis, P., Krupenia, S., Absér, A., Dimokas, N., Apoy, C., Karlsson, J., Larsson, A., Zidianakis, E., Efa, A., … Bischoff, S. (2020). Adaptive transitions for automation in cars, trucks, buses and motorcycles. IET Intelligent Transport Systems, 14(8), 889–899. https://doi.org/10.1049/iet-its.2018.5342 Huth, V., Biral, F., Martín, Ó., & Lot, R., 2012. Comparison of two warning concepts of an intelligent Curve Warning system for motorcyclists in a simulator study. Accident Analysis & Prevention, 44(1), 118-125. https://doi.org/10.1016/j.aap.2011.04.023 Kuschefski, A., Haasper, M., & Vallese, A. (2009). Advanced rider assistance systems for powered two-wheelers (ARAS-PTW). https://www-esv.nhtsa.dot.gov/Proceedings/22/files/22ESV-000123.pdf Pieve, M., Tesauri, F., & Spadoni, A. (2009, May). Mitigation accident risk in powered two wheelers domain: Improving effectiveness of human machine interface collision avoidance system in two wheelers. In 2009 2nd Conference on Human System Interactions (pp. 603-607). IEEE. Smith, T., Garets, S., & Cicchino, J. (2013). The effect of sight distance training on the visual scanning of motorcycle riders: A preliminary look. (Report No. DOT HS 811 689). Washington, DC: National Highway Traffic Safety Administration.
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