Next-generation vehicle control and future autonomous driving require further advances in vehicle dynamic state estimation. This article provides a concise review, along with the perspectives, of the ...recent developments in the estimation of vehicle dynamic states. The definitions used in vehicle dynamic state estimation are first introduced, and alternative estimation structures are presented. Then, the sensor configuration schemes used to estimate vehicle velocity, sideslip angle, yaw rate and roll angle are presented. The vehicle models used for vehicle dynamic state estimation are further summarized, and representative estimation approaches are discussed. Future concerns and perspectives for vehicle dynamic state estimation are also discussed.
Ethical decision-making during inevitable crashes, especially when humans involved, has become a big and sensitive roadblock for future mass adoption of autonomous vehicles. Towards addressing this ...challenge, this paper proposes a predictive control framework for ethical decision-making in autonomous driving using rational ethics. For flexibly implementing of ethical rules, the Lexicographic Optimization-based model predictive controller (LO-MPC) has been designed, in which obstacles and constraints are prioritized. Simulation environment is set up in PreScan, with different edge cases. The results show that the proposed LO-MPC approach has the capability to deal with the ethical decision-making during inevitable crashes by avoiding the obstacles with the assumed priority orders compared with traditional decision-making algorithm.
As the main component of an autonomous driving system, the motion planner plays an essential role for safe and efficient driving. However, traditional motion planners cannot make full use of the ...on-board sensing information and lack the ability to efficiently adapt to different driving scenes and behaviors of different drivers. To overcome this limitation, a personalized behavior learning system (PBLS) is proposed in this paper to improve the performance of the traditional motion planner. This system is based on the neural reinforcement learning (NRL) technique, which can learn from human drivers online based on the on-board sensing information and realize human-like longitudinal speed control (LSC) through the learning from demonstration (LFD) paradigm. Under the LFD framework, the desired speed of human drivers can be learned by PBLS and converted to the low-level control commands by a proportion integration differentiation (PID) controller. Experiments using driving simulator and real driving data show that PBLS can adapt to different drivers by reproducing their driving behaviors for LSC in different scenes. Moreover, through a comparative experiment with the traditional adaptive cruise control (ACC) system, the proposed PBLS demonstrates a superior performance in maintaining driving comfort and smoothness.
Wind erosion is one of the reasons for the formation of desertification in arid and semiarid areas. Many measures are used to achieve sustainable land management. Microcoleus vaginatus can influence ...and offer limited protection to soils from wind erosion through its impact on controlling threshold friction velocity. Therefore, the study aims to explore the effectiveness and anti-wind erosion ability of Microcoleus vaginatus with the aid of attapulgite-based nanocomposite and to find a method that can act as bioindicators for investigating wind erosion in arid and semiarid areas in the future, for offering a method to prevent desertification and provide a valuable measure for the sustainable development of the environment. In this study, the effects of wind stress on reactive oxygen species (ROS), malondialdehyde (MDA), superoxide dismutase (SOD), catalase (CAT), peroxidase (POD), glutathione (GSH) and the surface character of the soil were analysed. The results showed that increased ROS and MDA, decreased GSH, changed SOD, POD and CAT, and enhanced soil structure in Microcoleus vaginatus with the aid of attapulgite-based nanocomposites were influenced by 3 and 5 m·s
−1
wind erosion. Further analysis demonstrated that increased SOD, POD and CAT and decreased GSH eliminated ROS and MDA through the antioxidant defense response of Microcoleus vaginatus with the aid of attapulgite-based nanocomposites. The results revealed that Microcoleus vaginatus with the aid of attapulgite-based nanocomposite had an important physiological adaptation for the elimination of ROS and lipid peroxidation induced by wind stress and could play a role in alleviating wind erosion.
In this article, a novel approach of decision-making and motion control is designed for realizing safe and personalized driving of autonomous vehicles. A new lane-change intention generation model ...and a new lane-change decision-making algorithm are proposed. The feature of the proposed decision-making module is that the interactions between the ego vehicle and other surrounding vehicles are represented by the dynamic potential field (DPF) and embedded in the gap acceptance model to ensure the safety and personalization during driving. In addition, an integrated trajectory planning and tracking control algorithm, which incorporates the artificial potential field and constrained Delaunay triangulation (CDT) into the model predictive control framework, is developed. The newly developed integrated controller allows efficient execution of the expected motion. The proposed approach is tested under different driving conditions and further compared with an existing baseline method. The results show that the proposed approach is able to make safe and personalized decisions, and execute motion control more efficiently for automated driving under dynamic situations, validating its feasibility and effectiveness.
The recognition of driver's braking intensity is of great importance for advanced control and energy management for electric vehicles. In this paper, the braking intensity is classified into three ...levels based on novel hybrid unsupervised and supervised learning methods. First, instead of selecting threshold for each braking intensity level manually, an unsupervised Gaussian mixture model is used to cluster the braking events automatically with brake pressure. Then, a supervised Random Forest model is trained to classify the correct braking intensity levels with the state signals of vehicle and powertrain. To obtain a more efficient classifier, critical features are analyzed and selected. Moreover, beyond the acquisition of discrete braking intensity level, a novel continuous observation method is proposed based on artificial neural networks to quantitative analyze and recognize the brake intensity using the prior determined features of vehicle states. Experimental data are collected in an electric vehicle under real-world driving scenarios. Finally, the classification and regression results of the proposed methods are evaluated and discussed. The results demonstrate the feasibility and accuracy of the proposed hybrid learning methods for braking intensity classification and quantitative recognition with various deceleration scenarios.
To further improve learning efficiency and performance of reinforcement learning (RL), a novel uncertainty-aware model-based RL method is proposed and validated in autonomous driving scenarios in ...this paper. First, an action-conditioned ensemble model with the capability of uncertainty assessment is established as the environment model. Then, a novel uncertainty-aware model-based RL method is developed based on the adaptive truncation approach, providing virtual interactions between the agent and environment model, and improving RL's learning efficiency and performance. The proposed method is then implemented in end-to-end autonomous vehicle control tasks, validated and compared with state-of-the-art methods under various driving scenarios. Validation results suggest that the proposed method outperforms the model-free RL approach with respect to learning efficiency, and model-based approach with respect to both efficiency and performance, demonstrating its feasibility and effectiveness.
Dear Editor, With the development of automobile industry and artificial intelligence (AI) domains, autonomous vehicles (AVs) are becoming a reality and promise to revolutionize human mobility 1-3. ...The decision-making system of AVs is crucial, which is typically required to trade off multiple competing objectives. For example, when determining driving policies, autonomous electric vehicles (AEVs) need to consider two conflicting objectives: transport efficiency and electricity consumption. As one of state-of-the-art AI technologies, reinforcement learning (RL) has demonstrated its potential in a series of challenging tasks. Accordingly, RL has attracted considerable attention from global researchers 4.
The emerging automated driving technology poses a new challenge to driver-automation collaboration, which requires a mutual understanding between humans and machines through their intention ...identifications. In this article, oriented by human-machine mutual understanding, a driver steering intention prediction method is proposed to better understand human driver's expectation during driver-vehicle interaction. The steering intention is predicted based on a novel hybrid-learning-based time-series model with deep learning networks. Two different driving modes, namely, both hands and single right-hand driving modes, are studied. Different electromyography signals from the upper limb muscles are collected and used for the steering intention prediction. The relationship between the neuromuscular dynamics and the steering torque is analyzed first. Then, the hybrid-learning-based model is developed to predict both the continuous and discrete steering intentions. The two intention prediction networks share the same temporal pattern exaction layer, which is built with the bidirectional recurrent neural network and long short-term memory cells. The model prediction performance is evaluated with a varied history and prediction horizon to exploit the model capability further. The experimental data are collected from 21 participants of varied ages and driving experience. The results show that the proposed method can achieve a prediction accuracy of around 95% steering under the two driving modes.
With the aims of safe, smart and sustainable future mobility, a personalized approach of trajectory planning and control based on user preferences is developed for lane-change of autonomous vehicles ...in this paper. First, a safe area during the lane change process is identified by using constraint Delaunay triangulation. Then, an improved rapidly-exploring Random Trees ( i -RRT) is developed with B-spline to generate the feasible trajectory cluster, which is subject to the safe area boundaries and the vehicle dynamics. To extract a personalized trajectory from this cluster, we firstly adopt the fuzzy linguistic preference relation (FLPR) method to identify users' preferences on driving, which can be reflected by their subjective objectives including driving safety, ride comfort and vehicle stability. Then, the technique for order preference by similarity to ideal situation (TOPSIS) is utilized to solve the multi-objective optimisation problem formulated by considering the user preferences. The algorithms proposed above are integrated, and both simulation and experimental validation are conducted under lane-change scenarios of autonomous driving. Simulation and experiment results show that proposed approach is able to successfully realize personalized trajectory planning and lane-change control, satisfying users' various preferences and simultaneously ensure vehicle safety, demonstrating its feasibility and effectiveness.
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