End-to-End Autonomous Driving Risk Analysis: A Behavioural Anomaly Detection Approach

Ryan, Cian; Murphy, Finbarr; Mullins, Martin

doi:10.1109/tits.2020.2975043

Cited by 40 publications

(12 citation statements)

References 42 publications

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“…They recommended the technique utilized by every vehicle for finding abnormalities, and it depends on the conveyance of individual speed choices. Ryan et al [30] suggested a new method for quantifying AV injury risks by comparing them to the activities of humans. This proposed technique helps to evaluate the security level of AVs.…”

Section: Related Workmentioning

confidence: 99%

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Rajendar¹,

Kaliappan²

2022

Intelligent Automation &Amp; Soft Computing

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Section: Related Workmentioning

confidence: 99%

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Rajendar¹,

Kaliappan²

2022

Intelligent Automation &Amp; Soft Computing

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“…Pattern-based methods detect anomaly driving events by modeling driving maneuver patterns. Some of them identify cases where the driving behaviors depart from expected normal driving patterns, labeling them as abnormal events [5], [16]- [21]. Other studies have focused on detecting several specific types of abnormal driving maneuver patterns [3], [4], [22]- [29].…”

Section: A Driving Anomaly Detectionmentioning

confidence: 99%

Driving Anomaly Detection Using Conditional Generative Adversarial Network

Qiu¹,

Misu²,

Busso³

2022

Preprint

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“…For example, non-vision low-dimension data from vehicle sensors are utilized in [25] to to detect anomalies from pair-wise data correlation, such as between the acceleration and wheel torque. The approach proposed by [26] is framed in the end-to-end autonomous driving context. However, this approach detects for anomalies in vehicle control (steering, braking, and accelerating) through training a CNN model to learn the correlation between input driving video and human driving behavior.…”

Section: 𝜕𝐼 𝜕𝑥 (𝑥) +mentioning

confidence: 99%

Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

Feng

Easwaran

2021

ACM Trans. Embed. Comput. Syst.

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Uncertainties in machine learning are a significant roadblock for its application in safety-critical cyber-physical systems (CPS). One source of uncertainty arises from distribution shifts in the input data between training and test scenarios. Detecting such distribution shifts in real-time is an emerging approach to address the challenge. The high dimensional input space in CPS applications involving imaging adds extra difficulty to the task. Generative learning models are widely adopted for the task, namely out-of-distribution (OoD) detection. To improve the state-of-the-art, we studied existing proposals from both machine learning and CPS fields. In the latter, safety monitoring in real-time for autonomous driving agents has been a focus. Exploiting the spatiotemporal correlation of motion in videos, we can robustly detect hazardous motion around autonomous driving agents. Inspired by the latest advances in the Variational Autoencoder (VAE) theory and practice, we tapped into the prior knowledge in data to further boost OoD detection’s robustness. Comparison studies over nuScenes and Synthia data sets show our methods significantly improve detection capabilities of OoD factors unique to driving scenarios, 42% better than state-of-the-art approaches. Our model also generalized near-perfectly, 97% better than the state-of-the-art across the real-world and simulation driving data sets experimented. Finally, we customized one proposed method into a twin-encoder model that can be deployed to resource limited embedded devices for real-time OoD detection. Its execution time was reduced over four times in low-precision 8-bit integer inference, while detection capability is comparable to its corresponding floating-point model.

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End-to-End Autonomous Driving Risk Analysis: A Behavioural Anomaly Detection Approach

Cited by 40 publications

References 42 publications

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Sensor Data Based Anomaly Detection in Autonomous Vehicles using Modified Convolutional Neural Network

Driving Anomaly Detection Using Conditional Generative Adversarial Network

Improving Variational Autoencoder based Out-of-Distribution Detection for Embedded Real-time Applications

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