Optimizing Secure Vehicle Data Sharing in Autonomous Fleets with Blockchain and Reinforcement Learning

Cross-chain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration services for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) solutions for cross-chain data exchange across multiple domains are constrained by synchronous networks, hindering efficient data exchange in intermittent network environments. Furthermore, there is a lack of research on asynchronous cross-chain transaction pool mechanisms, which are crucial for optimizing system utility. In this paper, we propose AC-BaaS, an <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>synchronous <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">c</u>ross-<underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">b</u>lockchain <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u>s <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a</u> <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">s</u>ervice framework tailored for the multi-domain IoT. Built upon a specially designed asynchronous sidechain architecture, the system leverages a committee to provide AC-BaaS for data exchange across multiple IoT domains. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We further propose a heuristic for optimizing the utility of the buffer pool mechanism to strike a balance between performance and resource consumption. We also examine DAS delay size settings to trade-off security and efficiency. We analyze and prove the security of AC-BaaS, simulate asynchronous communication environments under various security levels, and conduct a comprehensive evaluation. The results show that AC-BaaS outperforms SOTA schemes, improving throughput by an average of 1.71 to 5.09 times, reducing transaction latency by 64.36% to 85.49%, and maintaining comparable resource overhead.

In recent years, the development of digital technology has driven the continuous improvement of the digital level of the international logistics industry. Shipping companies, port companies, and freight forwarding companies have all explored digital transformation, and international logistics business has become a trend toward full visibility, online transactions, and smart operations. In this context, the international logistics industry has created demands for convenience, efficiency, green environmental protection, smart flexibility, safety and reliability.At present, my country's Logistics data exchange lacks a unified standard and standardized data exchange platform as a whole. At the same time, there are also some attempts at data exchange, such as the four reports of a transportation vehicle. Through the cooperation and data exchange of the four departments, the operating efficiency of the transportation vehicle carrier has been improved.There are two major optimization directions in the future. One is to improve the quality and efficiency of data exchange through collaboration between all parties in the entire process; the other is to increase the utilization rate of data through the government's disclosure of the data in its hands. There is reason to believe that the future is the era of data. With the development of my country's Internet and information technology, data sharing and exchange will become our development direction and our future economic growth point.

Because of the strong request for data exchange under information environment, the existing information needs to be integrated and shared. And, the popularity of software-as-aservice (SaaS) caused that users need data integration. Traditional approach of data integration cannot accord with the complex information environment and cannot provide SaaS users with efficient data exchange and application integration. The paper proposes data integration as a service for Applications Deployment on the SaaS Platform that can hide heterogeneous data. It highlights the user's capability of accessing data. Compared with the traditional approach of data integration, it has many characteristics, such as expansibility, load balancing. It makes real-time data exchange and application integration possible. It improves the development of information resources and the integration of work flow.

In recent decades, group data sharing in cloud systems has been a prominent issue. With the rise in popularity of cloud computing, achieving safe and efficient data exchange in cloud settings is a pressing issue that must be addressed. Furthermore, achieving both anonymity and traceability in the cloud for data exchange is a difficulty. This work focuses on providing anonymous data exchange and storage in the cloud for the same group with good security and efficiency. A unique traceable group data sharing strategy is developed to allow anonymous multiple users in public clouds by leveraging the key agreement and the group signature. On the one hand, group members can communicate anonymously with regard to the group signature, and on the other hand, group members can track the anonymous members, chat within the group, store and share for secure instant access and updates.

To develop a health/medical data interchange model for efficient electronic exchange of data among health-checkup facilities. A Health-checkup Data Markup Language (HDML) was developed on the basis of the Standard Generalized Markup Language (SGML), and a feasibility study carried out, involving data exchange between two health checkup facilities. The structure of HDML is described. The transfer of numerical lab data, summary findings and health status assessment was successful. HDML is an improvement to laboratory data exchange. Further work has to address the exchange of qualitative and textual data.

Advanced planning for autonomous vehicles using reinforcement learning and deep inverse reinforcement learning

This paper presents the implementation of an autonomous electric vehicle (EV) project in the National Taiwan University of Science and Technology (NTUST) campus in Taiwan. The aim of this work was to integrate two important practices of realizing an autonomous vehicle in a campus environment, including vehicle positioning and path tracking. Such a project is helpful to the students to learn and practice key technologies of autonomous vehicles conveniently. Therefore, a laboratory-made EV was equipped with real-time kinematic GPS (RTK-GPS) to provide centimeter position accuracy. Furthermore, the model predictive control (MPC) was proposed to perform the path tracking capability. Nevertheless, the RTK-GPS exhibited some robust positioning concerns in practical application, such as a low update rate, signal obstruction, signal drift, and network instability. To solve this problem, a multisensory fusion approach using an unscented Kalman filter (UKF) was utilized to improve the vehicle positioning performance by further considering an inertial measurement unit (IMU) and wheel odometry. On the other hand, the model predictive control (MPC) is usually used to control autonomous EVs. However, the determination of MPC parameters is a challenging task. Hence, reinforcement learning (RL) was utilized to generalize the pre-trained datum value for the determination of MPC parameters in practice. To evaluate the performance of the RL-based MPC, software simulations using MATLAB and a laboratory-made, full-scale electric vehicle were arranged for experiments and validation. In a 199.27 m campus loop path, the estimated travel distance error was 0.82% in terms of UKF. The MPC parameters generated by RL also achieved a better tracking performance with 0.227 m RMSE in path tracking experiments, and they also achieved a better tracking performance when compared to that of human-tuned MPC parameters.

Reinforcement learning (RL) is one of the popular methods for intelligent control and decision making in the field of robotics recently. The goal of RL is to learn an optimal policy of the agent by interacting with the environment via trail and error. There are two main algorithms for RL problems, including model-free and model-based methods. Model-free RL is driven by historical trajectories and empirical data of the agent to optimize the policy, which needs to take actions in the environment to collect the trajectory data and may cause the damage of the robot during training in the real environment. The main different between model-based and model-free RL is that a model of the transition probability in the interaction environment is employed. Thus the agent can search the optimal policy through internal simulation. However, the model of the transition probability is usually estimated from historical data in a single environment with statistical errors. Therefore, an issue is faced by the agent is that the optimal policy is sensitive to perturbations in the model of the environment which can lead to serious degradation in performance. Robust RL aims to learn a robust optimal policy that accounts for model uncertainty of the transition probability to systematically mitigate the sensitivity of the optimal policy in perturbed environments. In this overview, we begin with an introduction to the algorithms in RL, then focus on the model uncertainty of the transition probability in robust RL. In parallel, we highlight the current research and challenges of robust RL for robot control. To conclude, we describe some research areas in robust RL and look ahead to the future work about robot control in complex environments.

&amp;lt;jats:p&amp;gt;Large amounts of data are collected on space bodies, and all data is available to all users free of charge through various archives and portals. When searching for data, especially non-space scientists, often have problems because the archives are tailored to meet only space experts needs. Prior knowledge about missions is necessary to search data and use archives. Large amounts of data, and the interest of the public impose the need to develop Spatial Data Infrastructure of Celestial Bodies to enable efficient use and exchange of data. In this paper user needs assessment, in the form of global survey, for the purpose of establishing SDICB was con-ducted. The results will serve as a basis for establishing guidelines for the implantation of the SDICB concept.&amp;lt;/jats:p&amp;gt;

Velocity control in car-following behavior with autonomous vehicles using reinforcement learning

Autonomous navigation at unsignalized intersections: A coupled reinforcement learning and model predictive control approach

Autonomous navigation in dense traffic scenarios remains challenging for autonomous vehicles (AVs) because the intentions of other drivers are not directly observable and AVs have to deal with a wide range of driving behaviors. To maneuver through dense traffic, AVs must be able to reason how their actions affect others (interaction model) and exploit this reasoning to navigate through dense traffic safely. This paper presents a novel framework for interaction-aware motion planning in dense traffic scenarios. We explore the connection between human driving behavior and their velocity changes when interacting. Hence, we propose to learn, via deep Reinforcement Learning (RL), an interaction-aware policy providing global guidance about the cooperativeness of other vehicles to an optimization-based planner ensuring safety and kinematic feasibility through constraint satisfaction. The learned policy can reason and guide the local optimization-based planner with interactive behavior to pro-actively merge in dense traffic while remaining safe in case other vehicles do not yield. We present qualitative and quantitative results in highly interactive simulation environments (highway merging and unprotected left turns) against two baseline approaches, a learning-based and an optimization-based method. The presented results show that our method significantly reduces the number of collisions and increases the success rate with respect to both learning-based and optimization-based baselines.

Reinforcement Learning (RL) has become a rapidly advancing field inside Artificial Intelligence (AI) and self-sufficient structures, revolutionizing the manner in which machines analyze and make selections. Over the past few years, RL has advanced notably with the improvement of more sophisticated algorithms and methodologies that address increasingly complicated actual-world troubles. This progress has been driven by using enhancements in computational power, the availability of big datasets, and improvements in machine-getting strategies, permitting RL to address challenges across a wide range of industries, from robotics and autonomous driving system to healthcare and finance. The effect of RL is evident in its capacity to optimize selection-making procedures in unsure and dynamic environments. By getting to know from interactions with the environment, RL agents can make decisions that maximize lengthy-time period rewards, adapting to converting situations and enhancing over time. This adaptability has made RL an invaluable tool in situations wherein traditional approaches fall brief, especially in complicated, excessive-dimensional spaces and behind-schedule remarks. This review aims to offer radical information about the current nation of RL, highlighting its interdisciplinary contributions and how it shapes the destiny of AI and autonomous technologies. It discusses how RL affects improvements in robotics, natural language processing, and recreation while exploring its deployment's ethical and practical demanding situations. Additionally, it examines key research from numerous fields that have contributed to RL's development.

To explore the impact of autonomous vehicles (AVs) on human-driven vehicles (HDVs), a solution for AV to coexist harmoniously with HDV during the car following period when AVs are in low market penetration rate (MPR) was provided. An extension car following framework with two possible soft optimization targets was proposed in this article to improve the experience of HDV followers with different following strategies by deep deterministic policy gradient (DDPG) algorithm. The pretreated Next Generation Simulation (NGSIM) dataset was used for the experiments. 1027 car following events with being redefined were extracted from it, in which 600 of the events were used for training and 427 of the events were used for testing. The different driving strategies obtained from the classical car following models were embedded into virtual environment built by OpenAI gym. The reward function combined safety, efficiency, jerk, and stability was used to encourage the agent with DDPG algorithm to maximize it. The final result reveals that disturbance of HDV followers decreases by 2.362% (strategy a), 8.184% (strategy b), and 13.904% (strategy c), respectively. The disturbance of HDV follower decreases by 14.961% (strategy a), 12.020% (strategy b), and 13.425% (strategy c), respectively. HDV followers with different strategies get less jerk in both soft optimizations. AV passengers get a loss on jerk and efficiency, but safety is enhanced. Also, AV car following performs better than HDV car following in both soft and brutal optimizations. Moreover, two possible solutions for harmonious coexistence of HDVs and AVs when AVs are in low MPR are proposed.

Urban transportation systems face increasing challenges due to rapid population growth, traffic congestion, and unpredictable road conditions. Traditional routing algorithms like Dijkstra and A* are limited in their ability to respond to real-time events such as accidents, roadwork, or weather disruptions. This study aims to develop a smarter, more adaptive route optimization system using machine learning techniques. The goal is to enhance travel time accuracy, reduce congestion, and improve commuter satisfaction through intelligent, data-driven decision-making. The proposed method integrates supervised learning for travel time prediction and reinforcement learning for real-time route selection, using data from GPS trajectories, traffic flow, weather reports, and user behaviors. A grid-based environment is used for reinforcement learning simulations, while OpenStreetMap data supports city-level route optimization. Results show that the machine learning-enhanced model significantly outperforms traditional algorithms in terms of adaptability, responsiveness, and reliability. In particular, reinforcement learning proved effective in dynamic environments, learning optimal routes over time and adjusting to disruptions. This research contributes to the development of intelligent transportation systems and supports the broader vision of smart cities, where mobility is safer, faster, and more efficient through the power of AI and real-time data integration.