VeMo: A Lightweight Data-Driven Approach to Model Vehicle Dynamics
This addresses the challenge of developing accurate vehicle models for autonomous driving applications on existing vehicles where structural information is often unavailable.
The paper tackled the problem of modeling vehicle dynamics under information scarcity by proposing a lightweight encoder-decoder model based on Gate Recurrent Unit layers, achieving a maximum mean relative error below 2.6% in extreme dynamic conditions.
Developing a dynamic model for a high-performance vehicle is a complex problem that requires extensive structural information about the system under analysis. This information is often unavailable to those who did not design the vehicle and represents a typical issue in autonomous driving applications, which are frequently developed on top of existing vehicles; therefore, vehicle models are developed under conditions of information scarcity. This paper proposes a lightweight encoder-decoder model based on Gate Recurrent Unit layers to correlate the vehicle's future state with its past states, measured onboard, and control actions the driver performs. The results demonstrate that the model achieves a maximum mean relative error below 2.6% in extreme dynamic conditions. It also shows good robustness when subject to noisy input data across the interested frequency components. Furthermore, being entirely data-driven and free from physical constraints, the model exhibits physical consistency in the output signals, such as longitudinal and lateral accelerations, yaw rate, and the vehicle's longitudinal velocity.