The emerging technological innovation system of driverless trucks

Engholm, Albin; Björkman, Anna; Joelsson, Yuri; Kristoffersson, Ida; Pernestål, Anna

doi:10.1016/j.trpro.2020.09.013

Cited by 13 publications

(4 citation statements)

References 8 publications

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“…This is also contradictory to the belief that truck drivers may lose their jobs over the self-driving technology. As mentioned in [56,57], driverless trucks can drastically reduce the driver costs, increase truck utilization, and improve truck safety. In spite of this, one can see that not enough research has been conducted on the impacts of self-driving trucks compared to passenger transport [58].…”

Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

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Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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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.

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Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

“…As mentioned in [57] above, according to Daimler's ex-CEO Zetsche, future vehicles need to have four characteristics: they need to be connected, autonomous, shared, and electric, the so-called CASE vehicles. Nevertheless, each of these points has the potential to turn the industry upside down.…”

Section: Stat2: Parameters For Ai Models (Trucks Vs Cars)mentioning

confidence: 99%

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Garikapati,

Shetiya

2024

BDCC

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“…Overall, the literature has discussed AT development relative to its potential benefits, technical progress, impacts on society, and challenges to adoption. Significant uncertainties about AT deployments remain [35]. However, as ATs mature, no top-down planning has occurred for deployments based on potential economic opportunities.…”

Section: Dong Et Al (mentioning

confidence: 99%

Ranking Opportunities for Autonomous Trucks Using Data Mining and GIS

Bridgelall,

Jones,

Tolliver

2023

Geographies

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The inefficiency of transporting goods contributes to reduced economic growth and environmental sustainability in a country. Autonomous trucks (ATs) are emerging as a solution, but the imbalance in the weight moved and ton-miles produced by long-haul and short-haul trucking creates a challenge in targeting initial deployments. This study offers a unique solution by presenting a robust method that combines data mining and geographic information systems (GISs) to identify the optimal routes for ATs based on a top-down approach to maximize business benefits. Demonstrated in a U.S. case study, this method revealed that despite accounting for only 16% of the weight moved, long-haul trucking produced 56% of the ton-miles, implying a high potential for ATs in this segment. The method identified eight key freight zones in five U.S. states that accounted for 27% of the long-haul weight and suggested optimal routes for initial AT deployment. Interstate 45 emerged as a pivotal route in the shortest paths among these freight zones. This suggests that stakeholders should seek to prioritize funding for infrastructure upgrades and maintenance along that route and the other routes identified. The findings will potentially benefit a broad range of stakeholders. Companies can strategically focus resources to achieve maximum market share, regulators can streamline policymaking to facilitate AT adoption while ensuring public safety, and transportation agencies can better plan infrastructure upgrades and maintenance. Users globally can apply the methodological framework as a reliable tool for decision-making about where to initially deploy ATs.

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“…Most stakeholders aim at improving road safety [1], reducing traffic congestion and emissions [2], and deploying universal mobility. The applications span from personal vehicles, robotaxis, and delivery vehicles to long-haul trucks, shuttles, and mining vehicles [3][4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Learning-Based Model Predictive Control for Autonomous Racing

Pinho

Costa

Lima

et al. 2023

WEVJ

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In this paper, we present the adaptation of the terminal component learning-based model predictive control (TC-LMPC) architecture for autonomous racing to the Formula Student Driverless (FSD) context. We test the TC-LMPC architecture, a reference-free controller that is able to learn from previous iterations by building an appropriate terminal safe set and terminal cost from collected trajectories and input sequences, in a vehicle simulator dedicated to the FSD competition. One major problem in autonomous racing is the difficulty in obtaining accurate highly nonlinear vehicle models that cover the entire performance envelope. This is more severe as the controller pushes for incrementally more aggressive behavior. To address this problem, we use offline and online measurements and machine learning (ML) techniques for the online adaptation of the vehicle model. We test two sparse Gaussian process regression (GPR) approximations for model learning. The novelty in the model learning segment is the use of a selection method for the initial training dataset that maximizes the information gain criterion. The TC-LMPC with model learning achieves a 5.9 s reduction (3%) in the total 10-lap FSD race time.

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The emerging technological innovation system of driverless trucks

Cited by 13 publications

References 8 publications

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Autonomous Vehicles: Evolution of Artificial Intelligence and the Current Industry Landscape

Ranking Opportunities for Autonomous Trucks Using Data Mining and GIS

Learning-Based Model Predictive Control for Autonomous Racing

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