Abstract It is a striking fact that the path tracking accuracy of autonomous vehicles based on active front wheel steering is poor under high-speed and large-curvature conditions. In this study, an adaptive path tracking control strategy that coordinates active front wheel steering and direct yaw moment is proposed based on model predictive control algorithm. The recursive least square method with a forgetting factor is used to identify the rear tire cornering stiffness and update the path tracking system prediction model. To adaptively adjust the priorities of path tracking accuracy and vehicle stability, an adaptive strategy based on fuzzy rules is applied to change the weight coefficients in the cost function. An adaptive control strategy for coordinating active front steering and direct yaw moment is proposed to improve the path tracking accuracy under high-speed and large-curvature conditions. To ensure vehicle stability, the sideslip angle, yaw rate and zero moment methods are used to construct optimization constraints based on the model predictive control frame. It is verified through simulation experiments that the proposed adaptive coordinated control strategy can improve the path tracking accuracy and ensure vehicle stability under high-speed and large-curvature conditions. 

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Adaptive Coordinated Path Tracking Control Strategy for Autonomous Vehicles with Direct Yaw Moment Control

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This article presents a trajectory following control solution for the lateral motion of an unmanned vehicle. The proposed solution is based on model predictive lateral control. The lateral motion is hard to control since it is nonlinear with large dynamics and uncertainties. By making a small angle approximation, the dynamic model can be linearized. A new bounded equivalent function based on the vehicle kinematic model and the Taylor series expansion is presented for trajectory following control solution for the lateral motion problem. The model predictive lateral control is used to ensure both strong robustness and control accuracy. Experiment results in a real environment are presented to show the effectiveness of the proposed method. 

Robust Lateral Trajectory Following Control of Unmanned Vehicle Based on Model Predictive Control

Accurate knowledge about the motion state of preceding vehicles (PVs) contributes to the optimization of planning and decision making of autonomous vehicles, which in turn further enhances road safety. Existing studies generally rely on information from special sensors mounted on the ego vehicle to estimate PVs state. With the evolution of information technology, the use of vehicle-to-vehicle (V2V) communication to estimate the PVs state has attracted more and more attention. However, the problem of how to reduce the communication rate while ensuring the estimation accuracy of PVs state with the limited communication bandwidth has not been addressed yet. In addition, traditional studies on lateral state estimation of PVs assume that the longitudinal velocity of the PV is known or design an additional estimator to predict the longitudinal velocity. This results in the dynamical coupling characteristics of the vehicle not being fully considered. To address these problems, an event-triggered estimation framework by fusing an event-triggered mechanism with an embedded cubature Kalman filter based on a coupled vehicle model is proposed for PVs state estimation. Simulation and real vehicle test results demonstrate that the proposed prediction approach can strike an effective balance between the communication rate and the estimation performance. The estimation accuracy of the proposed method is still superior to that of the cubature Kalman filter, even if the communication rate drops to 37.55%. 

An Event-Triggered Scheme for State Estimation of Preceding Vehicles Under Connected Vehicle Environment

Accurate knowledge about the motion state of preceding vehicles (PVs) contributes to the optimization of planning and decision making of autonomous vehicles, which in turn further enhances road safety. Existing studies generally rely on information from special sensors mounted on the ego vehicle to estimate PVs state. With the evolution of information technology, the use of vehicle-to-vehicle (V2V) communication to estimate the PVs state has attracted more and more attention. However, the problem of how to reduce the communication rate while ensuring the estimation accuracy of PVs state with the limited communication bandwidth has not been addressed yet. In addition, traditional studies on lateral state estimation of PVs assume that the longitudinal velocity of the PV is known or design an additional estimator to predict the longitudinal velocity. This results in the dynamical coupling characteristics of the vehicle not being fully considered. To address these problems, an event-triggered estimation framework by fusing an event-triggered mechanism with an embedded cubature Kalman filter based on a coupled vehicle model is proposed for PVs state estimation. Simulation and real vehicle test results demonstrate that the proposed prediction approach can strike an effective balance between the communication rate and the estimation performance. The estimation accuracy of the proposed method is still superior to that of the cubature Kalman filter, even if the communication rate drops to 37.55%.

A novel robust event‐triggered fault tolerant automatic steering control approach of autonomous land vehicles under in‐vehicle network delay

The adaptability of fish to their habitats is closely related to river hydraulics. While navigation channel projects aim to improve the navigation conditions by adjusting the hydrodynamic force of local river sections, the impacts of these projects on the hydraulic indicators of habitats of the four major Chinese carp species (FMCCs) remain unclear. Taking the Daijiazhou reach in the middle reaches of the Yangtze River as a case study, a mathematical model for spawning suitability was established to simulate changes in these hydraulic indicators before and after the implementation of a navigation channel project. The optimal flow rate interval for the spawning of the FMCCs was 17,500–22,000 m3·s−1. After the navigation channel project was implemented, the habitat suitability index (HSI) and weighted useable area (WUA) increased across the spawning habitats of all FMCCs, indicating that the project implementation created more habitat space. The central bar (Chihugang central bar) became exposed during the dry season, with the HSI and WUA decreasing under low water flow but increasing under medium-low or higher water flow levels. At the Daijiazhou bar head floodplain, which remained unexposed during the dry season, the HSI and WUA increased after project implementation, providing more space for spawning and habitation for the FMCCs. For the low point bar with bank gullies (Lejiawan point bar), the implementation of the bar protection zone project restricted gully development, with the HSI and WUA decreasing for all FMCCs. Based on the above impacts of navigation channel projects on the hydrodynamic environment of fish habitats, this paper provides a reference for the optimization of navigation channel arrangement as well as for the restoration of fish habitats.

https://www.mdpi.com/2073-4441/14/14/2147/pdf?version=1657099246

Simulating Ecological Effects of a Waterway Project in the Middle Reaches of the Yangtze River Based on Hydraulic Indicators on the Spawning Habitats of Four Major Chinese Carp Species

Chenyang Wang

Papers

A novel robust event‐triggered fault tolerant automatic steering control approach of autonomous land vehicles under in‐vehicle network delay

Simulating Ecological Effects of a Waterway Project in the Middle Reaches of the Yangtze River Based on Hydraulic Indicators on the Spawning Habitats of Four Major Chinese Carp Species