The automated driver as a new road user

Storsæter, Ane Dalsnes; Pitera, Kelly; McCormack, Edward

doi:10.1080/01441647.2020.1861124

Cited by 6 publications

(3 citation statements)

References 84 publications

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“…Easy to be affected by individual physiological and psychological states and driving styles, resulting in unstable maneuverability [11].…”

Section: Controlmentioning

confidence: 99%

How do Vehicle Automated Features Help or Hurt Driving Performance?

2023

EOIJ

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It is anticipated that vehicles having automated features of Level 0 to Level 5 will coexist in the future. However, many people are unsure what role, if any, human drivers will play at these levels. How do these automated features affect drivers' performances? This article attempts to answer this question by reviewing critical information from human-automation system characteristics of vehicles with specific automated features (AV). Essential facts about the differences in functional features between human drivers and systems and automated features at various levels were clarified and summarized, including their characteristics, roles, and technical AV structures. Finally, drivers’ performances at all automation levels were discussed. This review provides the insight needed to understand how the automated features affect drivers' performances and to what extent. The results indicate that drivers’ performance does not improve as the automated level upgrades. Compared with no automation, active-safety and high automation can achieve lower workload and better driving performances for drivers. In contrast, driver assistance and partial/conditional automation impose more increased workloads and unstable (even risky) driving performance.

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“…Easy to be affected by individual physiological and psychological states and driving styles, resulting in unstable maneuverability [11].…”

Section: Controlmentioning

confidence: 99%

How do Vehicle Automated Features Help or Hurt Driving Performance?

2023

EOIJ

View full text Add to dashboard Cite

show abstract

“…Therefore its maintenance plays an vital supporting role in the entire road life cycle [1]. In addition, lane marking ,with its color nature, is one of the recognition information for routing and behavior planning in current self-driving system [2][3]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

Network Optimization Based Lane Line Classification Study

Wang¹,

Deng²,

Sun³

et al. 2021

Preprint

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Efficient quality evaluation provides support for the timely and good maintenance of the lane line marking. This paper searches and optimizes the back propagation(BP) network model which referred to the analytic hierarchy process(AHP) model structure, as well as the number of nodes in the middle layer network. Based on this, a comprehensive evaluation method of multi-dimensional lane line quality such as shape, color and contrast is established. The experimental results show that the parameters of the model are more simplified, and the scoring and classification results of lane lines are more accurate.

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“…Clearly, the full potential of automated driving systems can only be realized when accompanied by infrastructure that possesses the appropriate level of technological advancement or "smartness". Hence, it is vital to understand the crucial role that infrastructure plays in autonomous driving [16,17]. Without appropriate infrastructure support, an AV that is considered to be at a certain SAE level in certain areas may not realize a similar level of autonomous driving performance in other areas.…”

mentioning

confidence: 99%

A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

et al. 2023

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To standardize definitions and guide the design, regulation, and policy related to automated transportation, the Society of Automotive Engineers (SAE) has established a taxonomy consisting of six levels of vehicle automation. The SAE taxonomy defines each level based on the capabilities of the automated system. It does not fully consider the infrastructure support required for each level. This can be considered a critical gap in the practice because the existing taxonomy does not account for the fact that the operational design domain (ODD) of any system must describe the specific conditions, including infrastructure, under which the system can function. In this paper, we argue that the ambient road infrastructure plays a critical role in characterizing the capabilities of autonomous vehicles (AVs) including mapping, perception, and motion planning, and therefore, the current taxonomy needs enhancement. To throw more light and stimulate discussion on this issue, this paper reviews, analyzes, and proposes a supplement to the existing SAE levels of automation from a road infrastructure perspective, considering the infrastructure support required for automated driving at each level of automation. Specifically, we focus on Level 4 because it is expected to be the most likely level of automation that will be deployed soon. Through an analysis of driving scenarios and state-of-the-art infrastructure technologies, we propose five sub-levels for Level 4 automated driving systems: Level 4-A (Dedicated Guideway Level), Level 4-B (Expressway Level), Level 4-C (Well-Structured Road Level), Level 4-D (Limited-Structured road Level), and Level 4-E (Disorganized Area Level). These sublevels reflect a progression from highly structured environments with robust infrastructure support to less structured environments with limited or no infrastructure support. The proposed supplement to the SAE taxonomy is expected to benefit both potential AV consumers and manufacturers through defining clear expectations of AV performance in different environments and infrastructure settings. In addition, transportation agencies may gain insights from this research towards their planning regarding future infrastructure improvements needed to support the emerging era of driving automation.

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The automated driver as a new road user

Cited by 6 publications

References 84 publications

How do Vehicle Automated Features Help or Hurt Driving Performance?

How do Vehicle Automated Features Help or Hurt Driving Performance?

Network Optimization Based Lane Line Classification Study

A Taxonomy for Autonomous Vehicles Considering Ambient Road Infrastructure

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