MS Thesis - High-Fidelity Motorcycle Simulator for Rider Behavior Research

Motorcycle safety research requires detailed and repeatable measurements of both rider behavior and vehicle dynamics, particularly

during demanding or safety-critical maneuvers.

A key challenge is understanding rider behavior immediately before hazardous events, as such knowledge is critical for developing

future rider-assistance and safety systems. Real road testing can provide valuable information, but it is constrained by safety,

repeatability, scenario control and data observability.

An existing motorcycle riding simulator integrates a physical riding platform, BikeSim vehicle dynamics, CARLA and Unreal Engine 5

visualization, a Python supervisory application, a C/C++ integration layer, sensors, steering force feedback and motion-platform

feedback. The basic closed-loop integration has been established. The next step is to improve physical consistency,

rider-perceived steering fidelity, vehicle response and end-to-end system performance, while creating a reliable experimental

data-acquisition platform.

The project will therefore focus on improving and validating the simulator as a human-in-the-loop research platform. The intended

result is a practical system that balances simulation realism, controllability, user experience, stability and experimental

repeatability, while collecting synchronized high-resolution data describing both the motorcycle and the rider.

The aim of this thesis is to improve and validate an existing motorcycle riding simulator as a high-fidelity experimental platform

for rider-behavior and pre-accident safety research. The work will investigate the relationship between simulator fidelity and

rider perception, with particular focus on rider trust, controllability, and behavioral realism. The resulting platform should

support synchronized acquisition of rider movement, steering input, interaction forces, vehicle dynamics, and virtual-environment

data.

• How can high-resolution rider and vehicle measurements be synchronized and recorded reliably across heterogeneous real-time

systems?

• How do simulator characteristics influence experienced riders’ perception of realism, controllability and trust in the

simulator response?

• Review and extend the current end-to-end signal chain between the physical platform, Python, CARLA/UE5, the C/C++ integration

layer and BikeSim.

• Improve vehicle-control behavior, including low-speed stability, steering-input mapping, lean response, corner entry and

recovery to upright riding.

• Use fixed-camera video for experiment documentation and review. Camera-based automatic pose estimation may be included as an

optional extension.

The development and verification process will be iterative. Issues identified during verification or validation will be corrected

and tested again. The rider experiment will begin only after the technical verification and scenario-based validation have been

completed successfully.

• System Familiarization

The student will become familiar with the existing simulator, including its components, data flow and operating workflow.

• System Improvement

The student will improve workflow and data exchange between Python, the C/C++ DLL, BikeSim and CARLA/UE5. The work will also

improve steering-input mapping, vehicle-control behavior and steering force feedback, including resistance, damping and

increased resistance at large steering angles.

• Component and Integration Verification

The student will verify that control functions and data transmission work correctly within each component and between

components. Timing, latency and synchronization will also be evaluated.

• Scenario-Based Validation

The complete simulator will be validated in selected riding scenarios to confirm that vehicle behavior, control responses and

system feedback are correct, consistent and repeatable.

• Rider Experiment

Experienced motorcycle riders will test the simulator in selected scenarios. Objective measurements and rider feedback will be

used to evaluate realism, controllability and the overall riding experience. The experiment will follow applicable requirements

for participant safety, informed consent, data protection and ethical approval.

Suitability

1-2 master’s thesis students with an engineering background.

Benefits and Learning Opportunities