Development of a Fall Detection Algorithm for Powered Two Wheelers Application

Giovannini, Federico; Baldanzini, Niccolò; Pierini, Marco

doi:10.4271/2014-32-0022

Cited by 6 publications

(5 citation statements)

References 11 publications

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“…The data analysis also highlights if the intervention of MAEB can cause some hazards for the rider or reduce his/her ability to maneuver in pre-crash situations. These can be done using the fall detection models available in the literature for PTWs [21] , [22] , [23] , 32] .…”

Section: Planned Data Analysismentioning

confidence: 99%

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking

et al. 2021

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Autonomous Emergency Braking (AEB) was proved to be an effective and reliable technology in reducing serious consequences of road vehicles crashes. However, the feasibility in terms of end-users’ acceptability for the AEB for motorcycles (MAEB) still has to be evaluated. So far, only Automatic Braking (AB) activations in straight-line motion and decelerations up to 2 m/s 2 were tested with common riders. This paper presents a procedure which provides comprehensive support for the design of new experiments to further investigate the feasibility of MAEB among end-users. Additionally, this method can be used as a reference for designing tests for other advanced rider assistance systems. A comprehensive literature review was carried out to investigate previous findings related to MAEB. After that, a series of pilot tests using an automatic braking device on an instrumented motorcycle were performed. The specifications for new AB experiments were defined (in terms of test conditions, participants requirements, safety measures, test vehicles and instrumentation). A test protocol was defined to test the system in different riding conditions and with different AB working parameters. A proposal for the data analysis was presented.

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Section: Planned Data Analysismentioning

confidence: 99%

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking

et al. 2021

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“…Loss of control may be limited to the occurrence of instability and of unsafe behaviors and not necessarily to total loss of control [14]. Concerning braking, safe braking may be defined as a maneuver that allows the vehicle to stop in an upright position, with small oscillations, where the driver maintains control of the handlebars with smooth movements [15]. In the literature, we could find only one single example of a definition of algorithms which can be to detect critical braking events (defined as potential loss of control cases with locked wheel) [15].…”

Section: Introductionmentioning

confidence: 99%

“…Concerning braking, safe braking may be defined as a maneuver that allows the vehicle to stop in an upright position, with small oscillations, where the driver maintains control of the handlebars with smooth movements [15]. In the literature, we could find only one single example of a definition of algorithms which can be to detect critical braking events (defined as potential loss of control cases with locked wheel) [15]. The parameters of those algorithms were optimized by using virtual data reproducing emergency braking maneuvers and calibrated by six 'near missed' accident cases of naturalistic riding data.…”

Section: Introductionmentioning

confidence: 99%

Loss of Control Prediction for Motorcycles during Emergency Braking Maneuvers Using a Supervised Learning Algorithm

et al. 2020

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The most common evasive maneuver among motorcycle riders and one of the most complicated to perform in emergency situations is braking. Because of the inherent instability of motorcycles, motorcycle crashes are frequently caused by loss of control performing braking as an evasive maneuver. Understanding the motion conditions that lead riders to start losing control is essential for defining countermeasures capable of minimizing the risk of this type of crashes. This paper provides predictive models to classify unsafe loss of control braking maneuvers on a straight line before becoming irreversibly unstable. We performed braking maneuver experiments in the field with motorcycle riders facing a simulated emergency scenario. The latter involved a mock-up intersection in which we generated conflict events between the motorcycle ridden by the participants and an oncoming car driven by trained research staff. The data collected comprises 165 braking trials (including 11 trials identified as loss of control) with 13 riders representing four categories of braking skill, ranging from beginner to expert. Three predictive models of loss of control events during braking trials, going from a basic model to a more advanced one, were defined using logistic regressions as supervised learning methods and using the area under the receiver operating characteristic (ROC) curve as a performance indicator. The predictor variables of the models were identified among the parameters of the vehicle kinematics. The best model predicted 100% of the loss of control and 100% of the full control cases. The basic and the more advanced supervised models were adapted for loss of control identification with time series data, and the results detecting in real-time the loss of control events showed excellent performance as well as with the supervised models. The study showed that expert riders may maintain stability under dynamic conditions that normally lead less skilled riders to a loss of control or falling events. The best decision thresholds of the most relevant kinematic parameters to predict loss of control have been defined. The thresholds of parameters that typically characterize the loss of control such as the yaw rate and front-wheel lock duration were dependent on the rider skill levels. The peak-to-root-mean-square ratio of roll acceleration was the most robust parameter for identifying loss of control among all skill levels.

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“…Policymakers, researchers, and manufacturers have been working to meet this safety challenge through regulation and R&D of new technologies aimed at making motorcycles safer. Anti-lock braking systems (ABS), combined brake systems (CBS), traction control (TC), electronically adjustable suspension, adaptive cruise control, adjustable vehicle riding modes, and speed-sensitive electronic steering stabilizers are examples of active safety systems currently available in the market while motorcycle autonomous emergency braking (M-AEB) is an example of a new technology under active development [8][9][10][11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Design of a Motorcycle Steering Damper for a Safer Ride

et al. 2020

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Powered-two-wheelers (PTWs) are increasingly popular because of their lower cost compared to cars, and therefore the riders’ exposure risk is increasing. Due to their complex dynamics characterized by high non-linearity and inherent instability, PTWs are more difficult to control compared to four-wheeled vehicles. Wobble is a high-frequency instability mode affecting the steering assembly of the PTW, and which often causes the rider to lose control and crash when it occurs. In this paper, we present the design of a new motorcycle semi-active steering damper integrated into the steering column and utilizing a magnetorheological fluid (MRF) for variable damping torque. An analytical model of the concept was first used to perform the preliminary sizing, followed by concept validation using a 3D FE multiphysics magnetic-fluid analysis. The final innovative design offers several advantages compared to traditional steering dampers: (i) a wide range of adjustable damping torque values, with a multiplication factor up to 10 with a maximum electrical current of 2 A; (ii) total integration into the motorcycle steering column enabled by its axial design and limited radius; (iii) a simple chamber geometry that allows for easy manufacture; (iv) longer seal life due to the absence of direct contact between seals and the MRF.

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Development of a Fall Detection Algorithm for Powered Two Wheelers Application

Cited by 6 publications

References 11 publications

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking

Investigating the feasibility of motorcycle autonomous emergency braking (MAEB): Design criteria for new experiments to field test automatic braking

Loss of Control Prediction for Motorcycles during Emergency Braking Maneuvers Using a Supervised Learning Algorithm

Design of a Motorcycle Steering Damper for a Safer Ride

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