Crash Themes in Automated Vehicles: A Topic Modeling Analysis of the California Department of Motor Vehicles Automated Vehicle Crash Database

Alambeigi, Hananeh; McDonald, Anthony D.; Tankasala, Srinivas R.

doi:10.48550/arxiv.2001.11087

Cited by 8 publications

(11 citation statements)

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“…It can be seen that there exists only system study for Apollo while bug/issue studies cover the three open-source ADS which we mentioned in §2.3. In addition, more existing works [39][40][41][42][43][44][45][46] perform empirical studies on the disengagement and accident reports for identifying the root causes or failure types, since these investigations are critical to understand the ADS safety performances in the real world.…”

Section: Discussionmentioning

confidence: 99%

“…The following works [39][40][41][42][43][44][45][46] all perform analysis on the reports from the California Department of Motor Vehicles (CADMV) [37], which involve disengagement and accident records on public roads. Specifically, a disengagement refers to a failure that requires human driver to take over the control of vehicles; an accident refers to a collision with other traffic participants.…”

Section: Disengagement/accident Report Studymentioning

confidence: 99%

“…Analysis of accident reports. The following works [43][44][45][46] identify the common accident types from accident reports. In [43], Alambeigi et al utilize probabilistic topic models to identify common crash patterns, e.g., driver-initiated transition crashes, sideswipe crashes during left-side overtakes.…”

Section: Disengagement/accident Report Studymentioning

confidence: 99%

“…The following works [43][44][45][46] identify the common accident types from accident reports. In [43], Alambeigi et al utilize probabilistic topic models to identify common crash patterns, e.g., driver-initiated transition crashes, sideswipe crashes during left-side overtakes. Boggs et al [44] and Leilabadi et al [45] apply text analysis to the accident reports, and they find that the accidents mostly occur when vehicles run in the automated mode, and the most frequent ADS crash type is the rear-end collision.…”

Section: Disengagement/accident Report Studymentioning

confidence: 99%

“…The analysis and classification of the disengagements based on different perspectives (e.g., modules) [39][40][41][42] The identification of the common accident types by different methods (e.g., text analysis) [43][44][45][46] other vehicles on the road. Moreover, the probability by which a disengagement can be transited to a collision is 68%.…”

Section: System Studymentioning

confidence: 99%

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A Survey on Automated Driving System Testing: Landscapes and Trends

Tang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

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Automated Driving Systems (ADS) have made great achievements in recent years thanks to the efforts from both academia and industry. A typical ADS is composed of multiple modules, including sensing, perception, planning and control, which brings together the latest advances in multiple domains. Despite these achievements, safety assurance of the systems is still of great significance, since the unsafe behavior of ADS can bring catastrophic consequences and unacceptable economic and social losses. Testing is an important approach to system validation for the deployment in practice; in the context of ADS, it is extremely challenging, due to the system complexity and multidisciplinarity. There has been a great deal of literature that focuses on the testing of ADS, and a number of surveys have also emerged to summarize the technical advances. However, most of these surveys focus on the system-level testing that is performed within software simulators, and thereby ignore the distinct features of individual modules. In this paper, we provide a comprehensive survey on the existing ADS testing literature, which takes into account both module-level and system-level testing. Specifically, we make the following contributions: (1) we build a threat model that reveals the potential safety threats for each module of an ADS; (2) we survey the module-level testing techniques for ADS and highlight the technical differences affected by the properties of the modules; (3) we also survey the system-level testing techniques, but we focus on empirical studies that take a bird's-eye view on the system, the problems due to the collaborations between modules, and the gaps between ADS testing in simulators and real world; (4) we identify the challenges and opportunities in ADS testing, which facilitates the future research in this field. CCS Concepts: • Computer systems organization → Embedded systems; • Software and its engineering → Software verification and validation; • Security and privacy → Systems security; • Computing methodologies → Artificial intelligence.

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Section: Discussionmentioning

confidence: 99%

Section: Disengagement/accident Report Studymentioning

confidence: 99%

Section: Disengagement/accident Report Studymentioning

confidence: 99%

Section: Disengagement/accident Report Studymentioning

confidence: 99%

Section: System Studymentioning

confidence: 99%

See 3 more Smart Citations

A Survey on Automated Driving System Testing: Landscapes and Trends

Tang¹,

Zhang²,

Zhang³

et al. 2022

Preprint

View full text Add to dashboard Cite

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A conceptual framework for automation disengagements

Nordhoff

2024

Sci Rep

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A better understanding of automation disengagements can lead to improved safety and efficiency of automated systems. This study investigates the factors contributing to automation disengagements initiated by human operators and the automation itself by analyzing semi-structured interviews with 103 users of Tesla’s Autopilot and FSD Beta. The factors leading to automation disengagements are represented by categories. In total, we identified five main categories, and thirty-five subcategories. The main categories include human operator states (5), human operator’s perception of the automation (17), human operator’s perception of other humans (3), the automation’s perception of the human operator (3), and the automation incapability in the environment (7). Human operators disengaged the automation when they anticipated failure, observed unnatural or unwanted automation behavior (e.g., erratic steering, running red lights), or believed the automation is not capable to operate safely in certain environments (e.g., inclement weather, non-standard roads). Negative experiences of human operators, such as frustration, unsafe feelings, and distrust represent some of the adverse human operate states leading to automation disengagements initiated by human operators. The automation, in turn, monitored human operators and disengaged itself if it detected insufficient vigilance or speed rule violations by human operators. Moreover, human operators can be influenced by the reactions of passengers and other road users, leading them to disengage the automation if they sensed discomfort, anger, or embarrassment due to the automation’s actions. The results of the analysis are synthesized into a conceptual framework for automation disengagements, borrowing ideas from the human factor's literature and control theory. This research offers insights into the factors contributing to automation disengagements, and highlights not only the concerns of human operators but also the social aspects of this phenomenon. The findings provide information on potential edge cases of automated vehicle technology, which may help to enhance the safety and efficiency of such systems.

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