The most basic requirement for autonomous vehicles is accurate environmental perception. However, the lack of onboard computing resources makes it difficult for such a vehicle to process all environmental information. Through the edge computing technology and 6G networks, vehicles can offload computing tasks to edge servers for execution, which alleviates the problem of insufficient onboard resources. The environmental perception task offloading problem for autonomous vehicles is studied. The purpose is to improve the environmental perception quality through reasonable task offloading. After assigning dynamic priorities to tasks generated by autonomous vehicles, the multi-vehicle and multi-server task offloading process is abstracted as a Markov decision process, and an offloading decision algorithm based on deep reinforcement learning is designed to select edge nodes for task execution to obtain better long-term benefits. The earliest deadline first algorithm is improved to consider the deadline and priority of each task, allowing the edge node to complete more high-priority tasks within the specified time. Experimental results show that the proposed method can guarantee the basic environmental perception requirements of every vehicle, and performs better than existing methods in terms of the total priority of the discarded tasks in each scheduling period and the completion rate of key tasks.

The stock index is an important indicator to measure stock market fluctuation, with a guiding role for investors’ decision-making, thus being the object of much research. However, the stock market is affected by uncertainty and volatility, making accurate prediction a challenging task. We propose a new stock index forecasting model based on time series decomposition and a hybrid model. Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) decomposes the stock index into a series of Intrinsic Mode Functions (IMFs) with different feature scales and trend term. The Augmented Dickey Fuller (ADF) method judges the stability of each IMFs and trend term. The Autoregressive Moving Average (ARMA) model is used on stationary time series, and a Long Short-Term Memory (LSTM) model extracts abstract features of unstable time series. The predicted results of each time sequence are reconstructed to obtain the final predicted value. Experiments are conducted on four stock index time series, and the results show that the prediction of the proposed model is closer to the real value than that of seven reference models, and has a good quantitative investment reference value.

As an irreversible trend, connected vehicles have become increasingly more popular. They depend on the generation and sharing of data between vehicles to improve safety and efficiency of the transportation system. Due to the open feature of the vehicular ad hoc network (VANET), it is possible for dishonest and misbehaving vehicles to disrupt traffic by transmitting false information. In recent years, misbehavior detection systems have been developed to detect the malicious behaviour, and machine learning methods have been employed to make the detection more accurately. However, existing misbehavior detection systems typically require a single entity (e.g., a central server) for centralized data collection and training. Model updates are restricted due to data privacy and high overhead of data communication, which reduces the defensive capability of misbehavior detection systems. In this paper, we propose a blockchain-based federated learning scheme to detect misbehavior, which is trained collaboratively by coordinating multiple distributed edge devices while ensuring data security and privacy. In addition, to further protect the privacy of the model on the blockchain, differential privacy with the Gaussian mechanism is leveraged to provide strict privacy protection. Common data falsification attacks are studied in this paper. The experimental results show that our proposed scheme is feasible and effective, and demonstrate that our scheme achieves satisfied accuracy and efficiency.

Safety is one of the major concerns in autonomous driving tasks, and enhancing the collision avoidance ability of connected and autonomous vehicles (CAVs) is an effective way to improve road safety. Most current autonomous driving algorithms make braking and stopping decisions in a traffic emergency. However, such decisions may not be optimal. In this paper, we study the optimal collision avoidance decision-making method for emergencies. To address this challenge, we propose a cooperative decision-making scheme for CAVs in an emergency. Unlike previous decision-making methods, our scheme enables vehicles to avoid collisions by indicating the optimal emergency destinations, which leads to a new task: the prediction of the optimal collision avoidance destination. In the proposed scheme, the potential collision avoidance destinations are evaluated based on a deep reinforcement learning (DRL) model, and we propose a safety evaluation map (SEM) to describe the evaluation results. The cooperative ability of CAVs is considered in the proposed scheme, and a well-designed reward function is applied to train the DRL model. Extensive experiments demonstrate that the proposed model can accurately evaluate potential collision avoidance destinations and is effective in reducing traffic accident rates and accident damage in various traffic emergencies compared to state-of-the-art baseline methods.