Synthesizing Vehicle Speed-Related Features with Neural Networks

Abstract

In today’s automotive industry, digital technology trends such as Big Data, Digital Twin, and Hardware-in-the-loop simulations using synthetic data offer opportunities that have the potential to transform the entire industry towards being more software-oriented and thus more effective and environmentally friendly. In this paper, we propose generative models to synthesize car features related to vehicle speed: brake pressure, percentage of the pressed throttle pedal, engaged gear, and engine RPM. Synthetic data are essential to digitize Hardware-in-the-loop integration testing of the vehicle’s dashboard, navigation, or infotainment and for Digital Twin simulations. We trained models based on Multilayer Perceptron and bidirectional Long-Short Term Memory neural network for each feature. These models were evaluated on a real-world dataset and demonstrated sufficient accuracy in predicting the desired features. Combining our current research with previous work on generating a speed profile for an arbitrary trip, where Open Street Map data and elevation data are available, allows us to digitally drive this trip. At the time of writing, we are unaware of any similar data-driven approach for generating desired speed-related features.