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The accuracy of the control model is essential for the effectiveness of model-based control methods. However, factors such as model simplification, parameter variations, and environmental noise can introduce inaccuracies in vehicle state descriptions, thereby compromising the precision of path tracking. This study introduces data-driven enhancements for an MPC-based path tracking controller in autonomous vehicles (DD-PTC). The approach consists of two parts: firstly, Kolmogorov–Arnold Networks (KANs) are utilized to estimate tire lateral forces and correct tire cornering stiffness, thereby establishing a dynamic predictive model. Secondly, Gaussian Process Regression (GPR) is deployed to accurately capture the unmodeled dynamics of the vehicle to form a comprehensive control model. This enhanced model allows for precise path tracking through steering control. The superiority of DD-PTC is confirmed through extensive testing on the Simulink-CarSim simulation platform, where it consistently surpasses normal MPC and Linear Quadratic Regulator (LQR) strategies, especially in minimizing lateral distance errors under challenging driving conditions. [ABSTRACT FROM AUTHOR]

Providing safe, smooth, and efficient trajectories for autonomous vehicles has long been a question of great interest in the field of autopiloting. In dynamic and ever-changing urban environments, safe and efficient trajectory planning is fundamental to achieving autonomous driving. Nevertheless, the complexity of environments with multiple constraints poses challenges for trajectory planning. It is possible that behavior planners may not successfully obtain collision-free trajectories in complex urban environments. Herein, this paper introduces spatio–temporal joint optimization-based trajectory planning (SJOTP) with multi-constraints for complex urban environments. The behavior planner generates initial trajectory clusters based on the current state of the vehicle, and a topology-guided hybrid A* algorithm applied to an inflated map is utilized to address the risk of collisions between the initial trajectories and static obstacles. Taking into consideration obstacles, road surface adhesion coefficients, and vehicle dynamics constraints, multi-constraint multi-objective coordinated trajectory planning is conducted, using both differential-flatness vehicle models and point-mass vehicle models. Taking into consideration longitudinal and lateral coupling in trajectory optimization, a spatio–temporal joint optimization solver is used to obtain the optimal trajectory. The simulation verification was conducted on a multi-agent simulation platform. The results demonstrate that this methodology can obtain optimal trajectories safely and efficiently in complex urban environments. [ABSTRACT FROM AUTHOR]

Photo‐thermal catalysis has been an attractive alternative strategy to promote chemical reactions for years, however, how light cooperates with thermal energy is still unclear. We meet this demand by exploring reaction mechanism via pressure dependency studies as well as H/D exchange experiments with HCOOH decomposition as a probe over a palladium nanoparticle (Pdn) and isolated Pd (Pd1) decorated LaCrO3/C3N4 composite catalyst, in which the H2 formation rate shows a first‐order dependence on HCOOH and inverse first‐order dependence on CO partial pressures no matter the reaction was driven by thermal or photo‐thermal energy. Additionally, negligible kinetic isotopic effects (KIEs: kH/kD) were determined under both dark and light conditions at 1.04 and 1.18 when the HCOOH was replaced by HCOOD. Besides, when the reactant HCOOH was further replaced by DCOOD, the KIE values of 1.55 (dark) and 1.92 (light) were obtained, which indicates that the HCOOH decomposition follows kinetically relevant (KR) of C−H bond rupture within HCOOH molecule under both thermal and photo‐thermal reaction conditions and the catalytic surface was found to be densely covered by CO based on the pressure dependency studies as well as the in situ Fourier transform infrared spectroscopy (FTIR) analysis. Clearly, the HCOOH decomposition driven by thermal and photo‐thermal energy follows the same reaction mechanism. Nevertheless, light induced hot electrons and the derived thermal effect do cause the enhancement of the reaction activity in some circumstances compared with bare thermal catalysis, which clarifies the confusion on cooperation mechanism of photo and thermal energies from the kinetic perspective. Hot electrons induced by photo‐illumination was confirmed by in situ FTIR CO chemisorption with ∼10 cm−1 redshift identified of the CO feature once light was introduced. Meanwhile, the photo thermal reaction system suffers from severe electron‐hole re‐combination at high reaction temperatures and make the thermal effect of photo irradiation dominant with respect to the effect at low reaction temperatures. This research provides insight to the mechanism on how photo‐thermal reaction works and draws attention to the photo‐thermal reaction process in boosting catalytic activity. [ABSTRACT FROM AUTHOR]

Keywords: HCOOH decomposition; photo-thermal reaction; reaction mechanism EN HCOOH decomposition photo-thermal reaction reaction mechanism 1 1 1 04/05/22 20220401 NES 220401 B Together photo- and thermal energy b promote catalytic reactions in a synergetic way. HCOOH decomposition, photo-thermal reaction, reaction mechanism Clear evidence of kinetically relevant consistency was found under both thermal and photothermal HCOOH decomposition reactions over a Pd/LaCrO SB 3 sb /C SB 3 sb N SB 4 sb composite. [Extracted from the article]