This paper proposes a control architecture for autonomous lane keeping by a vehicle. In this
paper, the vehicle dynamics consist of two parts: lateral and longitudinal dynamics …

In industrial applications where computational resources are finite and data noises are
prevalent, the representation power of deep neural networks (DNNs) is crucial. Traditional …

Model Predictive Control (MPC) has shown to be a successful method for many applications
that require control. Especially in the presence of prediction uncertainty, various types of …

Electrochemical reaction processes attract increasing attention as a promising chemical
process alternative to achieve green and sustainable chemical manufacturing due to its …

This paper investigates a cooperative motion planning problem for large-scale connected
autonomous vehicles (CAVs) under limited communications, which addresses the …

Maneuver planning, which plays a key role in selecting desired lanes and speeds, is an
essential element of autonomous driving. Generally, for a vehicle driving on a multilane …

Energy-efficient trajectory planning aims to optimize the economic performance for
autonomous vehicles on the premise of ensuring driving safety, which excavate the energy …

This paper presents an adaptive fault tolerant control approach for autonomous vehicles
(AV) under actuator or sensor faults to improve driving safety. A learning-based stochastic …

The SOTA methods proposed voxelization or pillarization to regularize unordered point
clouds, improving computing efficiency for LiDAR-based 3D object detection. However, they …

Uncertainty in the behavior of other traffic participants is a crucial factor in collision
avoidance for automated driving; here, stochastic metrics should often be considered to …