Browsing by Author "Lu, Chao"
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Item Open Access A hybrid motion planning framework for autonomous driving in mixed traffic flow(Elsevier, 2022-11-28) Yang, Lei; Lu, Chao; Xiong, Guangming; Xing, Yang; Gong, JianweiAs a core part of an autonomous driving system, motion planning plays an important role in safe driving. However, traditional model- and rule-based methods lack the ability to learn interactively with the environment, and learning-based methods still have problems in terms of reliability. To overcome these problems, a hybrid motion planning framework (HMPF) is proposed to improve the performance of motion planning, which is composed of learning-based behavior planning and optimization-based trajectory planning. The behavior planning module adopts a deep reinforcement learning (DRL) algorithm, which can learn from the interaction between the ego vehicle (EV) and other human-driven vehicles (HDVs), and generate behavior decision commands based on environmental perception information. In particular, the intelligent driver model (IDM) calibrated based on real driving data is used to drive HDVs to imitate human driving behavior and interactive response, so as to simulate the bidirectional interaction between EV and HDVs. Meanwhile, trajectory planning module adopts the optimization method based on road Frenet coordinates, which can generate safe and comfortable desired trajectory while reducing the solution dimension of the problem. In addition, trajectory planning also exists as a safety hard constraint of behavior planning to ensure the feasibility of decision instruction. The experimental results demonstrate the effectiveness and feasibility of the proposed HMPF for autonomous driving motion planning in urban mixed traffic flow scenarios.Item Open Access Hybrid-learning-based classification and quantitative inference of driver braking intensity of an electrified vehicle(IEEE, 2018-02-21) Lv, Chen; Xing, Yang; Lu, Chao; Liu, Yahui; Guo, Hongyan; Gao, Hongbo; Cao, DongpuThe 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.