Driverless Trucks Research Articles

Route Control and Behavior Decision of Intelligent Driverless Truck Based on Artificial Intelligence Technology

With the increase in global car ownership, the demand for traffic safety is very strong. Research shows that drivers account for more than 90% of global traffic accidents. Driverless cars can reduce traffic accidents caused for these reasons and greatly improve traffic safety. At the same time, driverless real-time path planning can select the best driving route for vehicles, reduce traffic congestion, and improve the efficiency of transportation. To sum up, driverless vehicles are considered an important solution to ensure traffic safety, improve traffic efficiency, reduce energy consumption and pollution, and change travel mode. An intelligent driverless vehicle is a key component of the intelligent transportation system, which organically combines various functions such as. Among them, path tracking and motion control play a very important role in intelligent driverless technology. At the same time, accurately tracking the desired feasible path and stable motion control are the basis of intelligent unmanned driving. Based on this, this paper uses artificial intelligence technology to study the path control and behavior decision-making of intelligent driverless trucks, and an improved tracking control method is proposed. Through this improved method, the intelligent unmanned vehicle can track the desired feasible path under different curvatures more accurately and stably. Finally, through the road test experiment of the intelligent unmanned vehicle experimental platform in the actual environment, the effectiveness of the scheme design and related algorithms of intelligent unmanned vehicle motion control in this paper is verified.

Effect of Connected and Autonomous Vehicles on Supply Chain Performance

This paper proposes a simulation framework that quantitatively assesses the direct and indirect effects of connected and autonomous vehicles (CAVs) on a supply chain system by varying the levels of CAV market penetration and driverless truck adoption. To quantify CAV effects on transportation network, this paper first collects secondary data and adopts simulation parameters and equations from existing literature. The results from transportation analysis are then incorporated into supply chain analysis to evaluate how CAVs would change supply chain performance measured by total travel time, greenhouse gas emissions, and supply chain cost. As the performance of supply chain systems involving perishable or semi-perishable products is highly sensitive to CAV market penetration rate and driverless truck adoption rate mainly because of reduced travel time, this paper uses fresh potato supply chain systems as an illustrative example. The case study results indicate that CAVs can greatly improve supply chain performance directly and indirectly by decreasing total travel time and supply chain costs, whereas emissions are reduced primarily through the adoption of driverless trucks in the supply chain system. The effect of CAVs on supply chain performance becomes even greater when commodities travel longer distances. This study will allow supply chain managers (and grocery delivery companies) to better understand how supply chain design and operation could be transformed and reoptimized in response to the introduction of CAV technologies. The research outcomes would help them better utilize the opportunities and address possible challenges that may arise as a result of CAVs.

Impacts of large-scale driverless truck adoption on the freight transport system

This paper presents an analysis of the potential impacts of large-scale adoption of driverless trucks on transport patterns and system costs for a national freight transport system with Sweden as a case study. The analysis is performed by extending the application domain of the Swedish national freight transport model Samgods to analyze two types of driverless truck scenarios. The first scenario represents a full adoption of driverless trucks that can operate the complete road network and thereby substitute manually driven trucks. In this scenario, road transport tonne-kilometers on Swedish territory increase by 22%, vehicle kilometers traveled by trucks increase by 35% and annual total system costs decrease by 1.7 B€ compared to a baseline scenario without driverless trucks. In the second scenario, the current fleet of manually driven trucks is complemented by driverless trucks that can operate on major roads between logistics hubs, but not in complex traffic environments like urban areas due to a limited operational design domain. This may be an initial use-case for driverless trucks operating on public roads. In this scenario, road tonne-kilometers increase by 11%, truck vehicle kilometers traveled increase by 15%, and annual total system costs decrease by 1.2 B€ compared to the baseline. For both scenarios, the impacts of driverless trucks vary significantly between commodity types and transport distances which suggests heterogeneity of benefits of automated driving between different types of freight flows. A sensitivity analysis is performed in which the costs for driverless truck operations is varied, and for the second scenario, also which parts of the road network that driverless trucks can operate are varied. This analysis indicates that the magnitude of impacts is highly dependent on the cost level of driverless trucks and that the ability for DL-trucks to perform international, cross-border transport is crucial for achieving reductions in system costs. An overarching conclusion of the study is that driverless trucks may lead to a significant increase in road transport demand due to modal shifts from rail and sea as a result of the improved cost performance of road transport. This would further strengthen the need to decarbonize road transport to meet non-negotiable climate targets. Important topics for future research include assessing potential societal costs related to driverless trucks due to infrastructure investments and negative externalities such as increasing CO2 emissions and congestion.

Autonomous Vehicle Path Planning using Q-Learning

Path planning is vital in autonomous vehicle technology, from robots to self-driving cars and driverless trucks, it is impossible to navigate without a proper path planning algorithm, various algorithms exist Q-learning being one of them. Q-learning is used extensively in discrete applications as it is effective in finding solutions to these problems. This research investigates the possibility of using Q-learning for solving the local path planning problem with obstacle avoidance. Q-learning is split into two phases, the first being the training phase, and the second being the application phase. During training, Q-learning requires exponentially increasing training time based on the system’s state space. However, when Q-learning is applied it becomes as simple as a lookup table which allows it to run on even the simplest microcontrollers. Two simulations are conducted with varying environments. One to showcase the ability to learn the optimal path, the other to showcase the ability for learning navigation in variable environments. The first simulation was run on a static environment with one obstacle, with enough training episodes, Q-learning could solve the path planning problem with minimal movement steps. The second simulation focuses on a randomized environment, obstacles and the agent’s starting position are randomly chosen at the start of every episode. During testing, Q-learning was able to find a path to the target when a path did exist, as It was possible in certain configurations for the vehicle to be stuck in between obstacles with no feasible path or solution.

How Will the 4th Industrial Revolution Influences the Extraction Industry?

Even the third industrial revolution has never been officially finished, from some time an expression Fourth Industrial revolutiongot viral. It has started in 2011 with a project of German government, called Industrie 4.0, which was initiated during the HanoverExpo in 2012. In 2013 an official report on that project was issued.The article explains the term of 4th industrial revolution and tries to foresee influence of that phenomena on extraction industry.The big challenge of digitalization and cybernetic systems implementation carries a large saving potential, estimated to astonishing321 Bio. USD till 2025 but also risks and challenges to overcome. Discussion in the paper includes the most important technologieswith their state of development and existing implementations including autonomous machines in both underground and open pitmining, technology of self-operating drilling rigs and driverless trucks in open pits.Briefly description of current situation and a picture of the mining industry after extended automatization is presented. It touchesalso the challenges of the new industry concept and highlights a road map for the full digital mine.

Capturing the swarm intelligence in truckers: The foundation analysis for future swarm robotics in road freight

Abstract A group of individual truckers can be regarded as a swarm intelligence system without central management. With the development of autonomous driving technology, trucker groups will be replaced by driverless vehicles. At that point, a swarm of truckers will become a swarm robotics system. Therefore, considering the design and control of an efficient swarm robotics system, it is essential to investigate the properties and model the behaviors of a swarm of truckers in advance. In this study, we probe the characteristics of both individual truckers and a swarm of truckers using trajectory data of truckers. First, the trajectory data were map matched based on the geographic scale of cities and administrative regions. Then, the properties of the division of labor, pattern formation, and swarm synchronization were obtained through an analysis of the spatiotemporal distribution of radius of gyration, travel distance, and the number of visited places. Because predicting the next visit locations of individuals of a swarm is a measure for modeling swarm behaviors, the prediction model can be used to predict future swarm robotics (driverless trucks) behaviors. Thus, we apply several machine learning models to predict the next locations of truckers. The results show that there are common characteristics and routines embodied in the behavior of the truckers; the swarm shows consistency and regularity. Moreover, the peak predictability of the entire group reached 94%, indicating that our model can predict the behavior of groups and individuals. Our findings provide basis supporting to the future efficient swarm robotics system.

Impacts of Large-Scale Driverless Truck Adoption on the Freight Transport System

Driverless trucks could potentially lead to a significant reduction in road freight transport costs and thereby change the freight transport system. In this paper, a method to study the impacts of large-scale adoption of driverless trucks on transport systems is presented and applied to the Swedish freight transport system. An analysis of the impacts on transport patterns and system costs, considering all transport modes, is performed. The method is based on extending the application domain of the Swedish national freight transport model Samgods to two types of driverless truck scenarios. The first scenario represents a full adoption of driverless trucks that can operate the complete road network. The results show that there is an increase in road transport tonne-kilometers on Swedish territory by 22% and vehicle kilometers traveled by trucks increase by 35%, compared to a baseline scenario without driverless trucks. The annual total transport system costs decrease by 1.7 B€. In the second scenario, the current fleet of manually driven trucks is complemented by driverless trucks that can operate between logistics hubs, but not in complex traffic environments like urban areas due to a limited operational design domain. This may be an initial use-case for driverless trucks operating on public roads. In this scenario, road tonne-kilometers increase by 11%, truck vehicle kilometers increase by 15%, and annual total transport system costs decrease by 1.2 B€ compared to the baseline. For both scenarios, the impacts of driverless trucks vary significantly across commodity types and transport distances which suggest heterogeneity of benefits of automated driving between different types of freight flows. A sensitivity analysis is performed for the level of cost reduction of driverless trucks compared to manually driven trucks for both scenarios. For the second scenario, also which sections of the road network that driverless trucks can operate are varied. The magnitude of system impacts are for both scenarios highly dependent on the cost level of driverless trucks and it is crucial to enable driverless trucks for international, cross-border transport to achieve economic benefits on a system-level. Driverless trucks may also have significant societal costs due to potential infrastructure investments and from negative externalities such as increasing CO2 emissions and congestion. These are important topics for future research. Finally, the even more road-dominant transport system resulting from an introduction of driverless trucks strengthens the need to decarbonize road transport to meet non-negotiable climate targets.

Driverless Trucks in Ten Years - What the Logistics Industry Needs

Driverless Trucks in Ten Years - What the Logistics Industry Needs

Cost Analysis of Driverless Truck Operations

Road freight transport is believed by many to be the first transport domain in which driverless (DL) vehicles will have a significant impact. However, in current literature almost no attention has been given to how the diffusion of DL trucks might occur and how it might affect the transport system. To make predictions on the market uptake and to model impacts of DL truck deployment, valid cost estimates of DL truck operations are crucial. In this paper, an analysis of costs and cost structures for DL truck operations, including indicative numerical cost estimates, is presented. The total cost of ownership for DL trucks compared with that for manually driven (MD) trucks has been analyzed for four different truck types (16-, 24-, 40-, and 64-ton trucks), for three scenarios reflecting pessimistic, intermediate, and optimistic assumptions on economic impacts of driving automation based on current literature. The results indicate that DL trucks may enable substantial cost savings compared with the MD truck baseline. In the base (intermediate) scenario, costs per 1,000 ton-kilometer decrease by 45%, 37%, 33%, and 29% for 16-, 24-, 40-, and 60-ton trucks, respectively. The findings confirm the established view in the literature that freight transport is a highly attractive area for DL vehicles because of the potential economic benefits.

The emerging technological innovation system of driverless trucks

Driving automation technology is attractive for the road freight transport sector since driverless trucks (DL-trucks) may drastically reduce driver costs, increase truck utilization and improve road safety. Although DL-trucks may bring significant impacts to the transport system, research on the future diffusion and impacts of DL-trucks is scarce compared to passenger transport. In this paper the sociotechnical innovation system developing, diffusing and utilizing DL-trucks in Sweden is analyzed based on the technological innovation systems (TIS) framework. The analysis is based on 20 expert interviews with a total of 23 representatives from 16 actors in the DL-truck TIS in Sweden. The TIS analysis shows that there are significant uncertainties in the timeline, operational capabilities, infrastructure requirements and regulative landscape for a widespread DL-truck deployment. There is a general view among the interviewees that DL-trucks is an important opportunity for Swedish industry and the economy. From a transport system perspective, DL-trucks are expected to bring sustainability benefits but it remains uncertain whether these benefits will be realized and what the negative side effects might be. The development of DL-trucks is heavily influenced by incumbent firms in the truck manufacturing industry but new actors from the telecom sector, energy sector and emerging truck technology companies are entering the area and shaping the development. The current relatively rigid institutions for truck manufacturing and road freight transport will require significant alignment to adapt to DL-truck operations in areas such as laws and regulations, business models and operational practices. The value chain of road freight transport may be disrupted as some of the current key actors, for instance traditional road carriers, could become less relevant in future DL-truck value chains. A critical uncertainty is how and by which actors the setting of requirements, deployment and financing of digital infrastructure for DL-trucks will be done.

Driverless Trucks at Ford: Cruising into a Compromised Brand Identity?

Ford's F-Series of trucks were first introduced in 1948, and ever since they have represented American identity for their consumers. Both earned media, in movies like Urban Cowboy, and Ford's paid media positioned Ford as part of the pioneering culture. Ford also constantly introduced innovations to the F-Series to make the trucks more suitable to the changing needs of its consumers.In 2018, Ford's management decided to retreat from the low-margin cars segment and focus on trucks and SUVs. Ford was also working toward robot taxis and driverless delivery by 2021. These two parallel trajectories converge to pose a pivotal challenge for Ford: Should the company invest in developing driverless capabilities for its best-selling and highest-margin product, the F-150? The case provides students with a context in which to discuss the changing technologies in the auto industry and their implications for industry structure, along with the specific aspects of software-driven business models, consumer preferences, and brand identity. It also offers an opportunity to explore the challenges faced by traditional businesses as they develop digital capabilities and reimagine their business models to fully leverage artificial intelligence (AI). The competition among Ford, Google Inc. (Google), Uber Technologies, Inc. (Uber), and Tesla, Inc. (Tesla) in the automonous vehicle industry highlights the different routes these companies have taken to obtain develop autonomous vehicle capability that leverages their respective strategic capabilities. Excerpt UVA-M-0967 Rev. Jul. 10, 2020 Driverless Trucks at Ford: Cruising into a Compromised Brand Identity? In the summer of 2018, iconic American automaker Ford Motor Company (Ford) created a new limited liability company, Ford Autonomous Vehicles LLC (Ford AV). Ford charged the organization “with accelerating [Ford's] AV business to capitalize on market opportunities.” As members of the newly appointed management team at Ford AV considered the best way to guide Ford, the second-largest automaker in the United States, toward its well-publicized robot-taxi and driverless delivery services slated to debut in 2021, they had several important decisions to make regarding the company's strategy and driverless car product offerings of the future. Alongside its goals in the self-driving space, Ford had made a seminal product decision several months earlier, when the company announced it was retreating from the American car business and dropping several sedans, such as the Fusion, Fiesta, and Taurus (while preserving the Mustang and Focus Active crossover launching in 2019), in order to improve its profit margin. The company's decision to turn away from its slow-selling car models and instead focus on its more lucrative line of trucks and SUVs was in line with Ford's long-standing reliance on the success of its trucks division, especially the F-Series (which included the classic F-150 pickup truck and “super duty” F-250 and -350 trucks) that the company had first released in 1948. . . .

Prospects of development of land driverless trucks

The paper is dedicated to the problem of creation and efficient use of driverless trucks. It also contains general analysis of foreign experience and development trends. The concept of FSUE “NAMI” for development of a platoon of driverless trucks for cargo transportation between specialized terminals on public roads is provided. A road train with virtual couplings where the lead vehicle has a driver and the rest are driverless is proposed as a base for the driverless platoon. The platoon is formed by such road trains, which ensures extended functional features, including the possibility to form platoons of road trains from different logistics centers and with different destinations, quick reforming of the platoon in the logistics centers, possibility to split the platoon when driving on public roads, higher precision of control over the platoon during driving, etc. It is also shown that in order to improve the efficiency of cargo transportation, driverless cargo platforms (trucks) shall be designed without a cabin. The paper also considers the ways to reduce the price of driverless trucks by creating a special road infrastructure, which will allow transfer of the most expensive subsystems of driverless vehicles to such infrastructure. Upon that, the following subsystems will remain within the driverless chassis: navigation, communication, chassis control and ADAS. It is noted that creation of such infrastructure will require high investments, therefore, gradual implementation of such systems is seen as rational. It is shown that the most appropriate area for these purposes is transregional cargo transportation. It is noted that the economic reasons for implementation of the proposed concept are as follows: significant reduction of the number of drivers; significant simplification of the driverless platform control system and reduction of the price of subsystems; no need for the driver’s cabin within the driverless platform; fuel economy when driving within a platoon. Power unit options for driverless cargo platforms are investigated and the reasons for selection of a particular power unit are given.

Driverless trucks: Taking hold of the wheel

Driverless trucks: Taking hold of the wheel

Interview with Deborah DiSanzo of IBM Watson Health

Interview with Deborah DiSanzo of IBM Watson Health

Mining Robotics and Media Change

Mining Robotics and Media Change