Fuel consumption is one of the main concerns for heavy-duty trucks. Predictive cruise control (PCC) provides an intriguing opportunity to reduce fuel consumption by using the upcoming road ...information. In this study, a real-time implementable PCC, which simultaneously optimizes engine torque and gear shifting, is proposed for heavy-duty trucks. To minimize fuel consumption, the problem of the PCC is formulated as a nonlinear model predictive control (MPC), in which the upcoming road elevation information is used. Finding the solution of the nonlinear MPC is time consuming; thus, a real-time implementable solver is developed based on Pontryagin’s maximum principle and indirect shooting method. Dynamic programming (DP) algorithm, as a global optimization algorithm, is used as a performance benchmark for the proposed solver. Simulation, hardware-in-the-loop and real-truck experiments are conducted to verify the performance of the proposed controller. The results demonstrate that the MPC-based solution performs nearly as well as the DP-based solution, with less than 1% deviation for testing roads. Moreover, the proposed co-optimization controller is implementable in a real-truck, and the proposed MPC-based PCC algorithm achieves a fuel-saving rate of 7.9% without compromising the truck’s travel time.
Advanced Air Mobility (AAM) envisages a sustainable, safe, convenient, and affordable air transport system. In socio-technical transition of AAM, there are a number of trade-offs in ecosystem that ...need to be studied. Three perspectives on economic feasibility are explored: first, based on history of VTOL services and value of time estimates, we discuss whether AAM can provide customers with competitive mobility services; second, what are the stakeholders' insights on the deployment of AAM; last, the experience in the development of autonomous driving technology, such as parallel intelligence, can inform future AAM research.
Advanced Air Mobility (AAM) is an innovative transportation system that will provide safe and efficient mobility of cargo, passenger and emergency services in low-altitude airspace. This paper ...highlights AAM's vision and addresses fundamental research challenges and opportunities, e.g., vertical lift aircraft technologies, infrastructure and operation, unmanned traffic management and system security. The overall structure of the AAM ecosystem is discussed from a technology and service perspective. The SEEDS evaluation criteria for the AAM system are explored in terms of promoting safety, sustainability and service quality during the development, verification and validation, and operation phases. As a key sector of the Mobility 5.0 paradigm, new air mobility needs to be integrated into multimodal transport and logistics systems to deliver diverse, sustainable and trustworthy solutions in the near future.
The research on driver fatigue detection is of great significance to improve driving safety. This paper proposes a real-time comprehensive driver fatigue detection algorithm based on facial landmarks ...to improve the detection accuracy, which detects the driver’s fatigue status by using facial video sequences without equipping their bodies with other intelligent devices. A tasks-constrained deep convolutional network is constructed to detect the face region based on 68 key points, which can solve the optimization problem caused by the different convergence speeds of each task. According to the real-time facial video images, the eye feature of the eye aspect ratio (EAR), mouth aspect ratio (MAR) and percentage of eye closure time (PERCLOS) are calculated based on facial landmarks. A comprehensive driver fatigue assessment model is established to assess the fatigue status of drivers through eye/mouth feature selection. After a series of comparative experiments, the results show that this proposed algorithm achieves good performance in both accuracy and speed for driver fatigue detection.
The deployment of large language models (LLMs) brings challenges to intelligent systems because its capability of integrating large-scale training data facilitates contextual reasoning. This paper ...envisions a revolution of the LLM based (Artificial) Intelligent Operating Systems (IOS, or AIOS) to support the core of automated vehicles. We explain the structure of this LLM-OS and discuss the resulting benefits and implementation difficulties.
Steer-by-wire system has replaced the conventional mechanical linkages with electronic actuators. In this article, a joystick is utilized to substitute the conventional steering wheel and study the ...variable steering ratio design methods in this new human–machine interface. With this structure of steer-by-wire system, the steering angle and torque ratio can be designed flexibly. A dynamic model of joystick of steer-by-wire system is built based on bilateral control scheme. Through comparing the vehicle steering performance of joystick with steering wheel, three conventional steering ratio design methods are studied that are speed-dependent, speed- and angle-dependent and constant yaw rate gain. The drawbacks are analysed by three kinds of conventional steering ratio, and a novel variable yaw rate gain steering ratio design method is investigated. A driving simulator is used to verify and compare these steering ratio design methods. The computer simulation and experimental test results demonstrate that the variable yaw rate gain steering ratio design method can effectively guarantee vehicle steering performance.
Automation has come a long way since the early days of mechanization, i.e., the process of working exclusively by hand or using animals to work with machinery. The rise of steam engines and water ...wheels represented the first generation of industry, which is now called Industry 1.0. Subsequently, Industry 2.0 witnessed the development of electric power and assembly lines. Later on, programmable logic controllers and Human Machine Interfaces (HMI) were the new productivity tools in Industry 3.0, which enabled precise and consistent production. In recent years, Industry 4.0 absorbed the latest technologies of Internet of Things (IoT), Artificial Intelligence (AI), and big data, making production processes integrated, interconnected, and smart. Nowadays, Industry 5.0 has been proposed, which emphasizes human-centric automation. Specifically, the new concept of automation in Industry 5.0, named Automation 5.0, is no longer about how to create machinery to replace humans. Instead, it aims to reach organic interactions and cooperation between humans and machines, meeting the goal of "6S" - Safety, Security, Sustainability, Sensitivity, Service, and Smartness 1-4 - and the overall objective of deploying automation for the better, human-friendly, and smarter industry.
It is important to understand real-world performance of fuel-saving technologies for heavy goods vehicles (HGVs). This paper concerns applying data mining to quantify the fuel-saving benefit of a ...low-rolling-resistance (LRR) tyre using in-service operational data. Two HGVs of the same specification were employed, with one using LRR tyres and the other conventional tyres. A smartphone-based data logger was developed to collect data from the HGVs’ operations on publics roads for three months. Data from vehicle Controller Area Network, smartphone sensors, weather and elevation databases were collected. A data mining methodology was developed to mine data segments representing dynamically similar driving conditions between the two HGVs, and compare their fuel consumption rates through fitting data to a theoretical vehicle fuel consumption model. It was found that at a 95% confidence level the LRR tyres exclusively brought about a fuel-saving benefit between 6.89% and 8.37% in typical UK motorway driving conditions.
This paper is concerned with the modelling of strategic interactions between the human driver and the vehicle active front steering (AFS) controller in a path-following task where the two controllers ...hold different target paths. The work is aimed at extending the use of mathematical models in representing driver steering behaviour in complicated driving situations. Two game theoretic approaches, namely linear quadratic game and non-cooperative model predictive control (non-cooperative MPC), are used for developing the driver-AFS interactive steering control model. For each approach, the open-loop Nash steering control solution is derived; the influences of the path-following weights, preview and control horizons, driver time delay and arm neuromuscular system (NMS) dynamics are investigated, and the CPU time consumed is recorded. It is found that the two approaches give identical time histories as well as control gains, while the non-cooperative MPC method uses much less CPU time. Specifically, it is observed that the introduction of weight on the integral of vehicle lateral displacement error helps to eliminate the steady-state path-following error; the increase in preview horizon and NMS natural frequency and the decline in time delay and NMS damping ratio improve the path-following accuracy.
PDF
