Bicycle Simulator Improvement and Validation

Shoman, Murad; Imine, Hocine

doi:10.1109/access.2021.3071214

Cited by 12 publications

(7 citation statements)

References 15 publications

(18 reference statements)

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“…In future work, the detailed analysis of all signals for all participants, the experimentation results, the answers to the subsequent questionnaire, and the eye-tracker videos will be investigated in order to study the behavior of the cyclists and the effect of road surface characteristics and geometries on safer user behavior. The measured parameters will be compared to the outputs of the PICS-L bicycle simulator [30][31][32] where the same experiment scenario was reproduced in order to validate the underlying mathematical model.…”

Section: Discussionmentioning

confidence: 99%

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

Shoman¹,

Imine²,

Johansson³

et al. 2023

Highlights Veh.

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In this paper, we present the instrumentation of a city bicycle with different sensors and devic-es in order to measure cyclists’ inputs (i.e., pedaling and steering) and the dynamical and kinematic properties of the bicycle. The instrumentation includes two tri-axial accelerometers, an Inertial Measurement Unit (IMU), GPS, a potentiometer, a laser scanner, a pedaling power meter, and speed and cadence sensors, in addition to a mobile eye tracker worn by the cyclists. After the instrumentation and adjustment of the sensors, a study was conducted in the city of Stockholm using the instrumented bicycle with the aim to evaluate cycling safety and comfort on snowy surface conditions. The outputs of this experiment will be employed further to study the interaction of cyclists with road infrastructure and other road users and their impact on cyclists’ behavior and cycling safety.

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Section: Discussionmentioning

confidence: 99%

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

Shoman¹,

Imine²,

Johansson³

et al. 2023

Highlights Veh.

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“…The proposed framework deals with the visual behaviour of cyclists to consider useful insights into objective factors in evaluating their ride style [59,63]. The experiments consist of one road test which includes different cycle tracks (combined cycle and vehicle routes, with or without specific separation signals, pedestrian, and cycle paths) and one simulated experiment.…”

Section: Discussionmentioning

confidence: 99%

“…From the cumulative analysis of attention and inattention, the differences in the frames between the various categories have been calculated. The focus was mainly directed towards the road, which recorded 78% of frames, as users tend to focus on both the pavement close to their location point and ahead so that they can ready and responsive to each vehicle manoeuvre (Table 2) [59]. The greatest number of inattention frames is directed towards the GPS sensor (AVERAGE = 61%) placed in the handlebar with its path monitoring display (Table 3).…”

Section: On-site Testmentioning

confidence: 99%

The Visual Behaviour of the Cyclist: Comparison between Simulated and Real Scenarios

et al. 2023

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Cyclists are one of the main categories of road users particularly exposed to accident risk. The increasing use of this ecological means of transport requires a specific assessment of cyclist safety in terms of traffic flow and human factors. In this study, a particular visual tracking tool has been used to highlight not only the main critical points of the infrastructure, where a high level of distraction is recorded, but also the various interactions with different road users (pedestrians, vehicles, buses, wheelchairs, cyclists). To confirm the critical aspects of the infrastructure and the trend of workload, a similar circuit was reproduced in a bicycle simulator, which also allowed a meaningful comparison of cycling behaviour. The innovative component of this paper is a comparison between a real test, held in Stockholm, and a simulator where the same scenario has been represented, in order to highlight the objective differences in behaviour. The cycling performance was also evaluated both from an objective point of view, with the count of frames related to each category of visualization, and from a subjective one, through the questionnaires. The results show the crossing as a critical aspect because only 4/3% fixation is required for both simulated and real tests to confirm the significance of the comparison between the two experiments. The high attention rate, resulting from frame-by-frame analysis, also points to a clear difference in the perception of users, who feel with a low workload.

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“…Visual and audio can benefit the cyclist when other stimulus effectors are working synchronously [16,17]. For example, if the visual displayed to the cyclist is on top of a mountain, then the effect experienced by the cyclist has to match with the view [18,19,20,21].…”

Section: Literature Reviewmentioning

confidence: 99%

A Fully Immersive Virtual Reality Cycling Training (vProCycle) and its Findings

Mahalil¹,

Yusof²,

Ibrahim³

et al. 2023

IJACSA

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Virtual reality (VR) technology is popularly applied in various sports training such as cycling, rowing, soccer, tennis and many more. In VR cycling, however, cyclists are not able to fully immerse themselves during the training due to the hardware and applications limitations required in the setup. In order to be fully immersed during the training, cyclists need to have similar effects to an outdoor training where they will experience cycling resistance, temperature effect, altitude, visual, and audio. For this reason, dedicated stimulus effectors or hardware are required to create these expected effects. On cycling resistance, a realistic cycling experience can be simulated by using a special device that simulates a resistance to the back wheel when cycling uphill in the VR simulation. In addition, the back wheel resistance would need to match the view displayed while paddling on an elevation slope. For higher immersion purposes, and the effect of temperature must be created that matches with the view visible in the display. For example, while the cyclist is on top of a virtual mountain, the cyclist would want to feel the effects of high altitude and low temperature. These stimulus effectors affect the realism experience while cycling in the VR simulation training. In the authors' previous papers, the setup using a combination of stimulus effectors including uphill elevation climb, altitude, temperature, interaction, visual, and audio were integrated into a product called vProCycle. The study tested on vProcycle was conducted with an assumption that virtual reality can enhance the experience of physical cycling training. The objective of this study is to determine whether or not vProCycle may improve cyclists' performance. This paper will discuss in detail the findings from data gathered during the experiment using vProCycle. More specifically, the findings are focused on the speed and the heart rate beats per minute which determine their performance improvement.

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Bicycle Simulator Improvement and Validation

Cited by 12 publications

References 15 publications

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

Measuring Cyclist’s Inputs, the Kinematic and Dynamic Properties of a City Bicycle, and Estimating the Road Profile via Sensor Fusion

The Visual Behaviour of the Cyclist: Comparison between Simulated and Real Scenarios

A Fully Immersive Virtual Reality Cycling Training (vProCycle) and its Findings

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