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Automated truck platooning (ATP) has gained growing attention due to its advantage in reducing fuel consumption and carbon emissions. However, it poses serious challenges to highway bridges due to the load effect of multiple closely spaced heavy-duty trucks on the bridge. In China, ATP also has great application prospects in the massive and ever-increasing highway freight market. Therefore, the load effects of ATP on bridges need to be thoroughly investigated. In this study, typical Chinese highway bridges and trucks were adopted. ATP load models were designed according to the current Chinese road traffic regulations. The load effects of ATP on highway bridges were calculated using the influence line method and evaluated based on the Chinese bridge design specifications. Results show that the load effect of ATP on bridges increases with the increase in the gross vehicle mass and the truck platooning size but decreases with the increasing inter-truck spacing and the critical wheelbase. The Grade-I (best quality standard) highway bridges are generally capable of withstanding the ATP loads, while caution should be exercised for other bridges. Strategies for preventing serious adverse impacts of ATP load on highway bridges are proposed.

The logistics industry is facing a transformation. Automated driving has been gaining importance in the commercial vehicle industry and trucks with SAE L4 are expected by 2030 for the hub-to-hub scenario. Driven by the research question of what the direct logistics environment of automated trucks will look like in 2030 a two-round Delphi-based scenario study was conducted for domestic goods transport in Germany. 19 projections were developed and evaluated by 27 experts from different industries. With completelinkage clustering, four logistics scenarios for 2030 were created. The results show that environmental and social sustainability as well as digitalization are expected to be the most important drivers. These include the shift to electric drive systems, improved working conditions, and increasing transparency and connectivity of the supply chain. The experts forecast an increase in the importance of software services and a continuing shortage of skilled workers. Rather controversial are the topics of charging infrastructure for electrified transport and the degree of automation of loading systems. Overall, the results provide a reliable basis for strategic decision-making in order to ensure the introduction of automated trucks into the logistics of the future and their surrounding environment.

Autonomous platooning technology has been considered a promising solution for reducing costs in the trucking industry. In trucking networks, the operation of autonomous truck platoons is constrained by many factors such as network topology, travel distance, and demand distribution, indicating the importance of deciding when and where to form and decompose platoons. This study investigates a novel location problem for platoon formation center (PFC) in a trucking network, which plays an important role in platooning operations. PFCs are specifically constructed as infrastructures for the formation and decomposition of truck platoons. Semi-automated truck platoons traveling between PFCs save labor and fuel. Therefore, each origin–destination (OD) pair in the trucking network can choose a beneficial transportation route via PFCs. This study aims to find the optimal PFC location scheme and OD demand routing scheme to minimize the total cost, which includes PFC construction, driver labor, and truck fuel costs. Accordingly, a mixed-integer linear programming model is developed. To effectively solve large-scale problems in practical applications, a variable neighborhood search algorithm embedded with a heuristic routing allocation algorithm is designed. We construct 20 instances with different characteristics, such as the number and distribution of customer nodes and PFC candidate nodes; construction cost of PFC candidate nodes; and amount and distribution of OD demand. We evaluate the efficiency of the algorithm based on the experimental results. Furthermore, the benefits of the new transportation mode are quantified, and planners are provided with insights into PFC construction.

Vehicle automation is linked to various benefits, such as increase in fuel and transport efficiency as well as increase in driving comfort. However, automation also comes with a variety of possible downsides, e.g., loss of situational awareness, loss of skills, and inappropriate trust levels regarding system functionality. Drawbacks differ at different automation levels. As highly automated driving (HAD, level 3) requires the driver to take over the driving task in critical situations within a limited period of time, the need for an appropriate human–machine interface (HMI) arises. To foster adequate and efficient human–machine interaction, this contribution presents a user-centered, iterative approach for HMI evaluation of highly automated truck driving. For HMI evaluation, a driving simulator study [n = 32] using a dynamic truck driving simulator was conducted to let users experience the HMI in a semi-real driving context. Participants rated three HMI concepts, differing in their informational content for HAD regarding acceptance, workload, user experience, and controllability. Results showed that all three HMI concepts achieved good to very good results in these measures. Overall, HMI concepts offering more information to the driver about the HAD system showed significantly higher ratings, depicting the positive effect of additional information on the driver–automation interaction.

Many modern vehicles collect inter-vehicle distance data from radar sensors as input to driver assistance systems. However, vehicle manufacturers often use proprietary algorithms to conceal the collected data, making them inaccessible to external individuals, such as researchers. Aftermarket sensors may circumvent this issue. This study investigated the use of low-cost radar sensors to determine inter-vehicle distances during real-world semi-automated truck platooning on two-way, two-lane rural roads. Radar data from the two follower trucks in a three-truck platoon were collected, synchronized and filtered. The sensors measured distance, relative velocity and signal-to-noise ratio. Dashboard camera footage was collected, coded and synchronized to the radar data, providing context about the driving situation, such as oncoming trucks, roundabouts and tunnels. The sensors had different configuration parameters, suggested by the supplier, to avoid signal interference. With parameters as chosen, sensor ranges, inferred from maximum distance measurements, were approximately 74 and 71 m. These values were almost on par with theoretical calculations. The sensors captured the preceding truck for 83–85% of the time where they had the preceding truck within range, and 95–96% of the time in tunnels. While roundabouts are problematic, the sensors are feasible for collecting inter-vehicle distance data during truck platooning.

This chapter discusses the issues specific to the automated driving systems and the near‐future market introduction, including a short history and the expected benefits of automated driving systems, and the significance of automated driving systems of road vehicles in Japan. Japan has a long history in the research and development of automated driving systems of not only passenger cars but also heavy trucks, transit buses, and small low‐speed vehicles. Fully automated vehicles have technological, legal, and institutional issues to overcome; therefore, the safety validation of fully automated vehicles will require tremendously long‐distance driving tests, which must be undertaken during almost all kinds of weather. The chapter discusses the introduction of automated truck platoons and automated small, low‐speed vehicles for vulnerable road users, that is, after an introduction is given about the population issues in Japan. Therefore, automation in these types of vehicles is very necessary.

The advent of autonomous vehicles has heralded a transformative era in transportation, reshaping the landscape of mobility through cutting-edge technologies. Central to this evolution is the integration of artificial intelligence (AI), propelling vehicles into realms of unprecedented autonomy. Commencing with an overview of the current industry landscape with respect to Operational Design Domain (ODD), this paper delves into the fundamental role of AI in shaping the autonomous decision-making capabilities of vehicles. It elucidates the steps involved in the AI-powered development life cycle in vehicles, addressing various challenges such as safety, security, privacy, and ethical considerations in AI-driven software development for autonomous vehicles. The study presents statistical insights into the usage and types of AI algorithms over the years, showcasing the evolving research landscape within the automotive industry. Furthermore, the paper highlights the pivotal role of parameters in refining algorithms for both trucks and cars, facilitating vehicles to adapt, learn, and improve performance over time. It concludes by outlining different levels of autonomy, elucidating the nuanced usage of AI algorithms, and discussing the automation of key tasks and the software package size at each level. Overall, the paper provides a comprehensive analysis of the current industry landscape, focusing on several critical aspects.

AGV forklift truck is a very widely used intelligent warehousing logistics equipment; as a logistics handling tool with a very high degree of intelligence, its stability in the working process has a very high requirement. For the study of dynamic stability, the most common method is to conduct a modal analysis of the structure and analyze the natural vibration frequency characteristics of the structure so as to judge its stability. From the perspective of the dynamic stability of the whole vehicle, Ansys Workbench software was used to analyze and study the mode of a special AGV forklift truck in a crankshaft production workshop, extract the first four natural frequencies and the first four vibration modes, analyze the deformation characteristics of the entire AGV forklift truck in the working process, and predict the risk of cargo falling. Further, the excitation frequency from the ground is calculated, and through the comparison and analysis with the first four-order natural frequency of the vehicle, the conclusion that the AGV forklift will not resonate in normal operation is obtained, which provides a reference for the dynamic stability analysis of AGV forklift truck.

In recent years, with the general trend of factory automation and intelligent development, automatic guided vehicles (AGV), as an important component of factory logistics handling systems, are gradually replacing manual forklifts. The AGV chassis control system and navigation and positioning system play an important role in the middle. The chassis control system can realize various complex actions, while the navigation and positioning system calculates the posture of the AGV in real time based on the acquired signals, providing a basis for accurate positioning and navigation. LiDAR can obtain its position information by emitting lasers to objects and recovering signals with an accuracy of centimeter level. However, in stereoscopic warehouses, LiDAR is easily affected by goods, and its accuracy is reduced. In addition, the space for forklifts to rotate in narrow aisles is small, which limits its use. To solve these problems, an AGV with a special omnidirectional chassis and using reflective columns for positioning is designed, which not only effectively reduces costs but also improves parking accuracy from 11.0 mm to 5.5 mm, ultimately improving safety.

Automated storage and retrieval systems are principal components of modern production and warehouse facilities. In particular, automated guided vehicles nowadays substitute human-operated pallet trucks in transporting production materials between storage locations and assembly stations. While low-level control systems take care of navigating such driverless vehicles along programmed routes and avoid collisions even under unforeseen circumstances, in the common case of multiple vehicles sharing the same operation area, the problem remains how to set up routes such that a collection of transport tasks is accomplished most effectively. We address this prevalent problem in the context of car assembly at Mercedes-Benz Ludwigsfelde GmbH, a large-scale producer of commercial vehicles, where routes for automated guided vehicles used in the production process have traditionally been hand-coded by human engineers. Such ad-hoc methods may suffice as long as a running production process remains in place, while any change in the factory layout or production targets necessitates tedious manual reconfiguration, not to mention the missing portability between different production plants. Unlike this, we propose a declarative approach based on Answer Set Programming to optimize the routes taken by automated guided vehicles for accomplishing transport tasks. The advantages include a transparent and executable problem formalization, provable optimality of routes relative to objective criteria, as well as elaboration tolerance towards particular factory layouts and production targets. Moreover, we demonstrate that our approach is efficient enough to deal with the transport tasks evolving in realistic production processes at the car factory of Mercedes-Benz Ludwigsfelde GmbH.