Lane Change and Merge Maneuvers for Connected and Automated Vehicles: A Survey

Bevly, David M.; Cao, Xiaolong; Gordon, Mikhail; Ozbilgin, Guchan; Kari, David; Nelson, Brently; Woodruff, Jonathan; Barth, Matthew; Murray, Chase; Kurt, Arda; Redmill, Keith; Özgüner, Ü.

doi:10.1109/tiv.2015.2503342

Cited by 231 publications

(87 citation statements)

References 72 publications

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“…We will not repeat the findings presented in these surveys, but only highlight risky behaviors of vehicles, pedestrians, drivers, motorcyclists and bicyclists, respectively. 1) Vehicle Behaviors: Potentially hazardous vehicle behaviors include overtaking, lane change, rear-ending, speeddriving and failure to obey traffic laws, where the first three are studied the most with related works summarized in [50], [57], [58]. Vehicle behavior prediction can be hard at highspeed or cluttered urban scenarios.…”

Section: B Implicit Factorsmentioning

confidence: 99%

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

145

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With recent advances in learning algorithms and hardware development, autonomous cars have shown promise when operating in structured environments under good driving conditions. However, for complex, cluttered and unseen environments with high uncertainty, autonomous driving systems still frequently demonstrate erroneous or unexpected behaviors, that could lead to catastrophic outcomes. Autonomous vehicles should ideally adapt to driving conditions; while this can be achieved through multiple routes, it would be beneficial as a first step to be able to characterize Driveability in some quantified form. To this end, this paper aims to create a framework for investigating different factors that can impact driveability. Also, one of the main mechanisms to adapt autonomous driving systems to any driving condition is to be able to learn and generalize from representative scenarios. The machine learning algorithms that currently do so learn predominantly in a supervised manner and consequently need sufficient data for robust and efficient learning. Therefore, we also perform a comparative overview of 45 public driving datasets that enable learning and publish this dataset index at https://sites.google.com/view/driveability-surveydatasets. Specifically, we categorize the datasets according to use cases, and highlight the datasets that capture complicated and hazardous driving conditions which can be better used for training robust driving models. Furthermore, by discussions of what driving scenarios are not covered by existing public datasets and what driveability factors need more investigation and data acquisition, this paper aims to encourage both targeted dataset collection and the proposal of novel driveability metrics that enhance the robustness of autonomous cars in adverse environments.

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Section: B Implicit Factorsmentioning

confidence: 99%

Is it Safe to Drive? An Overview of Factors, Metrics, and Datasets for Driveability Assessment in Autonomous Driving

Guo¹,

Kurup²,

Shah³

2020

IEEE Trans. Intell. Transport. Syst.

145

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“…Safety, efficiency, and convenience are three key concerns raised in recent studies on intelligent vehicles [1][2][3][4][5][6][7][8]. According to a World Health Organization report, up to 50 million people are injured or disabled in road accidents worldwide every year with 90% of deaths occurred in developing nations [9].…”

Section: Introductionmentioning

confidence: 99%

Implicit Personalization in Driving Assistance: State-of-the-Art and Open Issues

et al. 2020

IEEE Trans. Intell. Veh.

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In recent decades, driving assistance systems have been evolving towards personalization for adapting to different drivers. With considering personal driving preferences and characteristics, these systems become more acceptable and trustworthy. This paper presents a survey of recent advances in implicit personalized driving assistance. We classify the collection of work into three main categories: 1) personalized Safe Driving Systems (SDS), 2) personalized Driver Monitoring Systems (DMS), and 3) personalized In-vehicle Information Systems (IVIS). For each category, we provide a comprehensive review of current applications and related techniques along with the discussion of industry status, gains of personalization, application prospects, and future focal points. Several existing driving datasets are summarized and open issues of personalized driving assistance are also suggested to facilitate future research. By creating an organized categorization of the field, this survey could not only support future research and the development of new technologies for personalized driving assistance but also facilitate the use of these techniques by researchers within the driving automation community.

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“…Vehicle safety applications specifically require accurate positioning systems to improve vehicular navigation. This is the case of lane-level positioning and collision avoidance systems [7,8,9,10]. Nevertheless, the accuracy of GNSS receivers is often not enough.…”

Section: Introductionmentioning

confidence: 99%