Abstract
Multibody models built in commercial software packages, e.g., ADAMS, can be used for accurate vehicle dynamics, but computational efficiency and numerical stability are very challenging in complex driving environments. These issues can be addressed by using data-driven models, owing to their robust generalization and computational speed. In this study, we develop a deep neural network (DNN) based model to predict longitudinal-lateral dynamics of an autonomous vehicle. Dynamic simulations of the autonomous vehicle are performed based on a semirecursive multibody method for data acquisition. The data are used to train and test the DNN model. The DNN inputs include the torque applied on wheels and the vehicle’s initial speed that imitates a double lane change maneuver. The DNN outputs include the longitudinal driving distance, the lateral driving distance, the final longitudinal velocities, the final lateral velocities, and the yaw angle. The predicted vehicle states based on the DNN model are compared with the multibody model results. The accuracy of the DNN model is investigated in detail in terms of error functions. The DNN model is verified within the framework of a commercial software package CarSim. The results demonstrate that the DNN model predicts accurate vehicle states in real time. It can be used for real-time simulation and preview control in autonomous vehicles for enhanced transportation safety.
Highlights
Recent advances on neural networks have led to dramatic progress in solving many complex engineering problems, and demand for such solutions is increasing with time
An algorithm to obtain the data of longitudinal-lateral dynamics and a deep neural network (DNN) modelling approach were presented
It is significant that massive parallel computations can be performed by using GPUs that are especially suitable for the large volumes of data in autonomous vehicles
Summary
Recent advances on neural networks have led to dramatic progress in solving many complex engineering problems, and demand for such solutions is increasing with time. For preview control of autonomous vehicles in terms of accuracy and safety, it is critical to investigate the coupling between lateral and longitudinal dynamics for accurate data acquisition, which is necessary for deep learning modelling [12–14]. The precise modelling of longitudinal-lateral coupling involves complex and nonlinear relations between vehicle state variables, and using the resulting vehicle model is too costly for real-time applications. For this reason, most studies in the field of preview control mainly focus on simplified or few-degree-of-freedom models, which are constrained to avoid highly coupled dynamics [25,26].
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