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Practical Methodology for Motorcycle Safety Assessment Using Crash Data

Explore benefits of Autonomous Emergency Braking (AEB) for motorcycles through in-depth crash investigation data analysis, methods, and case studies. The study evaluates MAEB applicability, trigger mechanisms, and impact on collision scenarios.

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Practical Methodology for Motorcycle Safety Assessment Using Crash Data

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  1. A practical methodology using in-depth crash data to support the assessment of new motorcycle safety technologies Giovanni Savino1,2 & Trevor Allen1* 1Accident Research Centre, Monash University 2Department of Industrial Engineering, University of Florence, Italy Oct 2015

  2. Motorcycle safety: What we know • PTWs represent an increasing proportion of road fatalities and serious injuries in Australia

  3. Vehicle Safety Technologies: • The most effective safety strategies for motorcycles are likely to be those aimed at crash prevention or speed reduction in event of crash (eg. ABS, Rizzi et al. 2015) • Recent technologies for passenger cars aimed at crash prevention have shown to be effective. One of the most promising is Autonomous Emergency Braking or AEB (Fildes, et al., 2015, Accid. Anal. Prev., 81:24-29) • These safety technologies have potential benefit to motorcycle safety if they can be translated to motorcycles (Savino, 2013). However this is not straightforward due to differences in vehicle dynamics and crash scenarios.

  4. Purpose To assess the potential benefits of Autonomous Emergency Braking for Motorcycles (MAEB), using retrospective data from real world crash investigation of motorbike crashes.

  5. About MAEB 1. • Collision mitigation system made up of 2 modules: • Crash detection component • - restricted to cases where collision inevitable • - disengages if rider attempts action prior to collision without influencing maneoverability • ECU-controlled braking module • - produces autonomous/enhanced braking • - must be mild to avoid de-stabilising 2.

  6. Methods Study case selection & setting: • Crash investigation data from ‘MICIMS’ case-control study • Crashes occurred on a public road in Victoria, 6am-midnight • Riders seriously injured (hosp. admission), aged 18yrs or older Case numbers and data collected: • 123 cases from a trauma hospital Data available: - Rider questionnaire (self reported crash details) - Inspection of motorcycle - Inspection of crash site - Access to police report information (where possible) Melbourne 150km radius Vehicle Trajectories Speedestimations Contributing factors

  7. Methods: Algorithm for the evaluating MAEB applicability 123 Cases Collision object < 1m wide OR Motorcycle lean angle > 10 deg.* OR Loss of control prior to reaching obstacle Yes Single vehicle crash OR No obstacle in front of m’cycle No Unlikely applicable n=30 (24%) Yes Probably not OR No Possibly applicable Not applicable n=64 (52%) n=29 (24%) (* only when PTW travelling along a curve – do not consider last second swerve manoeuvres) 2 1 3

  8. ..Methods • n=64 eligible cases → 20 were simulated • Trajectories of vehicles prior to the crash were reconstructed via numerical 2D simulations using custom MatLabSimulink tool developed by G. Savino. • Triggering of Motorcycle AEB was computed with an algorithm that identifies inevitable collision states based on vehicle speeds, relative heading and position of Other Vehicle • Simulations aimed to identify trajectories and control actions of the vehicles in the last 2 seconds before the collision tool place. • Each simulated case was reviewed by a panel composed by G. Savino (Chief investigator, ABRAM project), T. Allen (Research fellow, MICIMS), and G. Rayner (Crash Investigator, MICIMS). This aimed to identify any discrepancies between the simulations and reconstructed cases. • .

  9. EXAMPLE 1: (Case AL-089) • Crash Scenario (questionnaire) • -Rider approaching traffic cont. urban intersection (middle lane) • -Car travelling in opp. dir. commenced R turn into the path of m/cycle • -Rider applied front brakes only, then swerved to avoid vehicle • -Rider struck vehicle, catapulting off car onto footpath (25m – site inspection) • -Visual obstructions: No (rider perspective) • -Weather conditions: Light rain • -Lighting conditions: Dusk/dark, Street lights ON • -Road Surface: Wet (confirmed)

  10. EXAMPLE 1: Case #129 (AL-089) • Basic Crash Details (Site & Bike Inspection) • DCA code 121 – ‘right through’ crash, motorcyclist as ‘through’ vehicle • Est. travel speed of motorcycle (approaching int): 55-67 km/hr (70% confid.) • Excessive use of brake: No • Anti-lock brakes fitted: No (from bike inspection) • Basic Simulation details • Initial M’cycle speed assumed: 61 km/h. • Other vehicle (car) speed assumed: 30 km/h, decreasing to 12 km/h • (accel. 0.2 g) • Car turning radius = 16m Did MAEB trigger? Yes Triggering occurred 0.35 s before collision Impact speed reduction: 2.0 km/h

  11. EXAMPLE 2: (Case AL-009) Crash Scenario (crash investigation) - Rider travelling SE in far R lane (closest to centre median) in 80 km/h zone - Car travelling SW from side street -entered intersection, crossed 2 lanes into path of rider - M’cyclestruck front of motorcar. - Rider travelled 34m from point of impact - M’cyclecalc. speed: 63-77 km/h (75 % confid.) - Excessive use of brakes: not reported - No visual obstructions (stationary) - Dry road - Rider avoidance action: no recall R =39 m D= 7.5 m ? 11

  12. EXAMPLE 2: Case # 9 (AL-009) • Basic Simulation details • DCA code 110 • M’cycle travel speed 68 km/h • Car turning at assumed constant speed of 25 km/h. (radius 39m) R =39 m Did MAEB trigger? Yes Triggering occurred 0.38 s before collision Impact speed reduction: 4.0 km/h D= 75 m 12

  13. Results • In 18 out of 20 cases the Motorcycle AEB would have triggered according to the simulation • Time to collision (TTC) ranged from 0.23 to 0.56 sec Review process • At the end of the review process, 11 out of 20 cases were modified to better reflect the evidence derived from the crash investigation performed within MICIMS study. • Changes in the simulation parameters were recommended, mainly related to impact points and speed profiles, and occasionally vehicle trajectories.

  14. Limitations • Missing or inaccurate information from rider questionnaire or site inspection • Misjudgement of precipitating factors by crash investigator • Errors in trajectory reconstructions (qualitative and quantitative) • However, the method proposed by Savino et al. (2014) is especially designed to handle quantitative uncertainty in the reconstructed pre-crash trajectories.

  15. Conclusions • Avail. info. from the 20 selected cases together with the review process in panel discussions, were typically sufficient to model the crash scenarios for the purposes of assessing Motorcycle Automated Emergency Braking Future directions • AEB modelled in the numerical environment: simulations with and without assistance of AEB will be run to predict the effects that this safety technology may have produced in the reconstructed cases, including any changes in crash likelihood or impact speeds.

  16. Acknowledgements: ‘MICIMS’ case-control study Giovanni Savino (ABRAM) Chief Investigators: Lesley Day (Principal) Mike Lenne Mark Symmons Stuart Newstead Peter Hillard Rod McClure Project Team Rob Jackel Geoff Rayner Josie Boyle Research Partners:

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