Graphical abstract COVIDSum ( COVID -19 scientific paper Sum marization) consists of four major modules: (1) Dataset Preprocessing, (2) Heuristic Sentence Extraction, (3) Word Cooccurrence Graph Construction, and (4) Linguistically Enriched Abstractive Summarization. The Data Preprocessing module retrieves abstract and textual content of each paper and removes papers which have missed abstracts or are not written in English language. Sentence Extraction module applies three heuristic methods to extract sentences of each paper. Word Co-occurrence Relationship Graph Construction module extracts word co-occurrence relationship to construct an un-weighted directed word co-occurrence graph. Linguistically Enriched Abstractive Summarization module proposes a hybrid summarization approach, which utilizes SciBERT and a GATbased graph encoder to encode the word sequences and word co-occurrence graphs respectively, adopts highway networks to fuse the above two encodings for obtaining context vectors of sentences, and applies Transformer decoder to generate summaries.

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Sideslip angle estimation is vital to the safety and active control of autonomous vehicles. In this paper, an innovative vehicle kinematic-based sideslip angle estimation method is proposed. The method is built on multi-sensor fusion, which fuses the information of inertial measurement unit, global navigation satellite system (GNSS), and onboard sensors to continuously estimate the attitude and velocity of the vehicle and thus obtain the sideslip angle. The invariant extended Kalman filter framework is adopted and the left-invariant form is derived to implement the GNSS measurement update. In order to solve the unobservability problem when only a single GNSS receiver is equipped, the vehicle motion constraint is introduced into the filter to improve the accuracy of heading angle estimation. The method is validated by field test and the results show that the method is robust in terms of converge efficiency. Furthermore, the sideslip angle estimation accuracy is satisfactory with the average absolute error less than 0.25°, which meets the active safety control requirements for autonomous driving.

Vehicle steering control is crucial to autonomous vehicles. However, unknown parameters and uncertainties of vehicle steering systems bring a great challenge to its control performance, which needs to be tackled urgently. Therefore, this paper proposes a novel model free controller based on reinforcement learning for active steering system with unknown parameters. The model of the active steering system and the Brushless Direct Current (BLDC) motor is built to construct a virtual object in simulations. The agent based on Deep Deterministic Policy Gradient (DDPG) algorithm is built, including actor network and critic network. The rewards from environment are designed to improve the effectiveness of agent. Simulations and testbench experiments are implemented to train the agent and verify the effectiveness of the controller. Results show that the proposed algorithm can acquire the network parameters and achieve effective control performance without any prior knowledges or models. The proposed agent can adapt to different vehicles or active steering systems easily and effectively with only retraining of the network parameters.

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