This research provides a coordinated control framework for integrating lateral and longitudinal controls that improve tracking performance and overall robustness and could accurately predict lateral distance, speed, and yaw angle with an average absolute percentage error of less than 3%, demonstrating its effectiveness in predicting vehicle dynamics.
This work introduces a Model Predictive Control (MPC) path tracking controller, which is developed to boost robustness, tracking precision, and vehicle stability when navigating high-speed and high-curvature driving scenarios. First, a 3-degree-of-freedom (3-DOF) dynamic model of the vehicle is established to serve as...
Hanzhengnan Yu, Xiao-Yi Hou, Hao Zhang et al.· SAE technical paper series· 0 citations
An adaptive MPC-based lateral path-tracking strategy with a speed-scheduled prediction horizon, in which the longitudinal speed is treated as a measurable time-varying parameter, is proposed.
Haojie Huang, Fei Liu, Chenyu Wang et al.· Journal of the Brazilian Soc...· 0 citations
To solve the conflict between trajectory tracking and stability control for distributed drive electric vehicles under complex driving conditions, an integrated longitudinal and lateral coordinated control strategy based on a hierarchical architecture is proposed in this paper. First, a stability-boundary dataset is con...
Danhua Chen, Jie Hu, Yuting Liu et al.· Mathematics· 0 citations
For distributed drive electric vehicles, designing an effective automatic lane keeping system and optimizing wheel torque allocation to enhance overall performance have consistently been a key research focus in this field. However, such systems face multiple challenges in practical applications, including external dist...
Yao-Yang Chen, You-Qun Zhao, Danyang Li et al.· Proceedings of the Instituti...· 0 citations
The DDPG-DLPID, an adaptive motion controller, including a Deep Deterministic Policy Gradient (DDPG) reinforcement learning that can acquire the parameters of PID controllers is introduced, which achieves faster response and higher tracking accuracy, while substantially reducing tracking errors under interference condi...
Ling Wang, Yan Shi· SAE technical paper series· 0 citations
This study addresses target curvature tracking for lateral vehicle motion under low-friction driving conditions. To this end, a reinforcement learning-based steering control framework was developed for steer-by-wire vehicles using a curvature-based steering target. The target curvature was generated from the driver’s s...
Yujin Kim, Jaepoong Lee, Minjun Kim et al.· IEEE Access· 0 citations
Learn how Google DeepMind and GovTech Singapore uses lightweight on-device Gemma models to intercept conversational scams in private chats with zero cloud data egress.
MIT News · Artificial Intelligence· news.mit.eduOct 13, 2026
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
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