Analysis of Autonomous Vehicles Accidents Based on DMV Reports

Ma, Yuan; Yang, Shichun; Lu, Jiayi; Feng, Xinjie; Kwantwi, Louis Boafo; Cao, Yaoguang

doi:10.1109/cac57257.2022.10056056

Cited by 4 publications

(2 citation statements)

References 8 publications

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“…Nevertheless, accidents involving autonomous vehicles have been reported as a result of errors in the perception computing layers [1][2][3]. One such example involved an accident between one of Uber's vehicles and a pedestrian holding a bike while crossing a street.…”

Section: Introductionmentioning

confidence: 99%

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

Reis,

Machado da Silva,

Correia

2023

Remote Sensing

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The increased demand for and use of autonomous driving and advanced driver assistance systems has highlighted the issue of abnormalities occurring within the perception layers, some of which may result in accidents. Recent publications have noted the lack of standardized independent testing formats and insufficient methods with which to analyze, verify, and qualify LiDAR (Light Detection and Ranging)-acquired data and their subsequent labeling. While camera-based approaches benefit from a significant amount of long-term research, images captured through the visible spectrum can be unreliable in situations with impaired visibility, such as dim lighting, fog, and heavy rain. A redoubled focus upon LiDAR usage would combat these shortcomings; however, research involving the detection of anomalies and the validation of gathered data is few and far between when compared to its counterparts. This paper aims to contribute to expand the knowledge on how to evaluate LiDAR data by introducing a novel method with the ability to detect these patterns and complement other performance evaluators while using a statistical approach. Although it is preliminary, the proposed methodology shows promising results in the evaluation of an algorithm’s confidence score, the impact that weather and road conditions may have on data, and fringe cases in which the data may be insufficient or otherwise unusable.

show abstract

Section: Introductionmentioning

confidence: 99%

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

Reis,

Machado da Silva,

Correia

2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…Nevertheless, accidents involving autonomous vehicles have been reported as resulting from errors of the perception computing layers [1][2][3]. One such example involves an accident between one of Uber's vehicles and a pedestrian holding a bike while crossing a street.…”

Section: Introductionmentioning

confidence: 99%

An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector

Reis¹,

Silva²,

Correia³

2023

Preprint

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The increased demand and use of autonomous vehicles and advanced driver-assistance systems has been constrained by an incidence of accidents involving errors with the perception layer’s functionality. In tandem, recent papers have noted the lack of standardized, independent testing formats and insufficient methods with which to analyze, verify and qualify LiDAR-based data and categorization. While camera-based approaches benefit from an ample amount of research, camera images can be unreliable in situations with impaired visibility such as dim lighting and fog. This paper aims to introduce a novel method based entirely on LiDAR data with the capability to detect anomalous patterns as well as complementing other performance evaluators using a Copula-based approach. With a promising set of preliminary results, this methodology may be used to evaluate an algorithm’s confidence score, the impact conditions may have on LiDAR data and detect cases in which LiDAR data may be insufficient or otherwise unusable.

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A BEV Scene Classification Method based on Historical Location Points and Unsupervised Learning

Lu,

Yang,

Zhang

et al. 2023

2023 7th CAA International Conference on Vehicular Control and Intelligence (CVCI)

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Analysis of Autonomous Vehicles Accidents Based on DMV Reports

Cited by 4 publications

References 8 publications

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

An Introduction to the Evaluation of Perception Algorithms and LiDAR Point Clouds Using a Copula-Based Outlier Detector

An Introduction to the Evaluation of Perception Algorithms & LiDAR Point Clouds using a Copula-based Outlier Detector

A BEV Scene Classification Method based on Historical Location Points and Unsupervised Learning

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