The University of Tokyo · Engineering
Professor Shuo Cheng's research lab specializes in advanced vehicle dynamics control and active safety systems for autonomous driving. The lab focuses on developing robust, model-based control strategies—particularly model predictive control (MPC), H∞ control, and nonlinear estimation techniques—to enhance vehicle stability, path tracking, and collision avoidance under uncertain and time-varying conditions. Key research directions include integrated steering and braking control, side-slip angle estimation, and human-machine cooperative driving systems that balance automation with driver comfort and safety.
Figures are computed from collected data and may differ slightly.
The automated steering control technology is crucial for an autonomous vehicle, but due to parametric uncertainties and time varying, the performance of automated steering control can be degraded. Therefore, a vehicle automated steering controller based on a model predictive control (MPC) approach is proposed in this article. First, considering tire nonlinear characteristics, the state and control matrices are modified, then the time-varying vehicle speed is considered and a linear parameter var
The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via auto brake, and it is one of the key technologies of autonomous vehicles. Moreover, the vehicle lateral stability is very crucial in emergency scenarios. Due to complex traffic conditions and various road frictions, emergency brake may cause a vehicle to lose its lateral stability. Therefore, this paper proposes a lateral-stability-coordinated collision avoidance control system (LSCA
Obstacle avoidance systems for autonomous driving vehicles have significant effects on driving safety. The performance of an obstacle avoidance system is affected by the obstacle avoidance path planning approach. To design an obstacle avoidance path planning method, firstly, by analyzing the obstacle avoidance behavior of a human driver, a safety model of obstacle avoidance is constructed. Then, based on the safety model, the artificial potential field method is improved and the repulsive field
Autonomous vehicles’ dynamics stability control is one key issue to ensure safety of self-driving. However, vehicle uncertainties and time-varying parameters could weaken the performance of autonomous vehicle stability control. Therefore, this article proposes a novel robust linear matrix inequality (LMI)-based <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H$ </tex-math></inline-formula> -infinite feedback a
Vehicle side-slip angle is crucial for various vehicle active safety applications, but measuring it directly needs expensive measurement instruments and the vehicle nonlinear dynamics, parameters uncertainty, and sensor noise cause difficulties in its observation. Therefore, the accurate, affordable side-slip angle estimator is essential. Thus, a novel adaptive square-root cubature Kalman filter (ASCKF)-based estimator with the integral correction fusion is proposed. First, the square-root cubat
It is a difficult and important project to coordinate active front steering (AFS) and direct yaw moment control (DYC), which has great potential to improve vehicle dynamic stability. Moreover, the balance between driver’s operation and advanced technologies’ intervention is a critical problem. This paper proposes a human-machine-cooperative-driving controller (HMCDC) with a hierarchical structure for vehicle dynamic stability and it consists of a supervisor, an upper coordination layer, and two
Adaptive cruise control (ACC) is one of key technologies of advanced driver assistance systems. Challenges still need to be solved to improve the performance of ACC, such as ensuring vehicle dynamic stability during car following processes on curved roads, and driving comfort. Therefore, a multiple-objective ACC (MOACC) integrated with direct yaw moment control (DYC) is proposed to ensure vehicle dynamic stability and improve driving comfort on the premise of car following performance, and its h
A pyrolysis study of oil sludge with and without oil sludge ash and quartz sand as solid heat carriers was conducted using a laboratory-scale reactor and thermogravimetric analyzer. The effects of the pyrolysis temperature and solid heat carrier on the product distribution and oil quality were investigated. The results of the oil sludge ash addition case were compared to those of the quartz sand case to evaluate the possibility of using oil sludge ash as a solid heat carrier in the oil sludge py
The lane-change decision-making module of automated and connected vehicles (ACVs) is one of the most crucial and challenging issues to be addressed. Motivated by human beings' underlying driving paradigm and the convolutional neural network's (CNN) dramatic capability of extracting features and learning strategies, this article proposes a CNN-based lane-change decision-making method via the dynamic motion image representation. Human drivers take proper driving maneuvers after they subconsciously
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