Artificial intelligence and mechanical engineering are two mature fields of science that
intersect more and more often. Computer-aided mechanical analysis tools, including …

A precise vehicle dynamics model is critical for simulation and algorithm testing. Neural
networks have been widely used to build high-fidelity vehicle dynamics models due to the …

Autonomous container trucks (ACTs) contribute significantly to transportation efficiency in
ports. Their dynamics modeling is an indispensable component for constructing advanced …

Abstract Machine learning methods have proven to be powerful in various research fields. In
this paper, we show that research on radiation effects could benefit from such methods and …

An essential aspect of intelligent driving systems is the automatic lane-changing function.
However, in real-world traffic situations, the initially planned lane-changing trajectory can …

In a complex vehicle system, the monitoring of dynamic load is difficult work. Therefore, a
novel deep learning architecture based on a divide-and-conquer strategy is developed to …

Model Predictive Controller (MPC) is widely used as a technique for path tracking control
since it allows for dealing with system constraints and future forecasts. However, the …

Driver-automation shared control has recently proven to be a promising technical scheme to
enhance the safety of vehicles, but desire to improve the user acceptance of the shared …

We propose an improved DNN modeling method based on two optimization algorithms,
namely the linear decreasing weight particle swarm optimization (LDWPSO) algorithm and …

We describe a framework that can integrate prior physical information, eg, the presence of
kinematic constraints, to support data-driven simulation in multibody dynamics. Unlike other …