What humanlike errors do autonomous vehicles need to avoid to maximize safety?

Mueller, Alexandra S.; Cicchino, Jessica B.; Zuby, David S.

doi:10.1016/j.jsr.2020.10.005

Cited by 59 publications

(22 citation statements)

References 12 publications

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“…This is a controversial issue. Statistics [548] show that AVs are very safe, and AV-caused crashes are rare. It is still hard to get an accurate self-driving car death toll since some AVs are still developing or testing.…”

Section: Relation To Avsmentioning

confidence: 99%

Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

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This paper explores deep learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasising driving safety for both autonomous vehicles and human‐operated vehicles. A typical processing pipeline is presented, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilisation, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modelling, and anomaly detection. The main goal is to guide traffic analysts to develop their own custom‐built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL‐based algorithms proposed for each step. Existing open‐source tools and public datasets that can help train DL models are also reviewed. To be more specific, exemplary traffic problems are reviewed and required steps are mentioned for each problem. Besides, connections to the closely related research areas of drivers' cognition evaluation, crowd‐sourcing‐based monitoring systems, edge computing in roadside infrastructures, automated driving systems‐equipped vehicles are investigated, and the missing gaps are highlighted. Finally, commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems are reviewed.

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Section: Relation To Avsmentioning

confidence: 99%

Deep learning serves traffic safety analysis: A forward‐looking review

Razi

Chen

et al. 2022

IET Intelligent Trans Sys

View full text Add to dashboard Cite

show abstract

“…This is a controversial issue. Statistics [449] show that AVs are very safe, and AV-caused crashes are rare. It is still hard to get an accurate self-driving car death toll since some AVs are still developing or testing.…”

Section: Relation To Avsmentioning

confidence: 99%

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

Razi¹,

Chen²,

Li³

et al. 2022

Preprint

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This paper explores Deep Learning (DL) methods that are used or have the potential to be used for traffic video analysis, emphasizing driving safety for both Autonomous Vehicles (AVs) and human-operated vehicles. We present a typical processing pipeline, which can be used to understand and interpret traffic videos by extracting operational safety metrics and providing general hints and guidelines to improve traffic safety. This processing framework includes several steps, including video enhancement, video stabilization, semantic and incident segmentation, object detection and classification, trajectory extraction, speed estimation, event analysis, modeling and anomaly detection. Our main goal is to guide traffic analysts to develop their own custom-built processing frameworks by selecting the best choices for each step and offering new designs for the lacking modules by providing a comparative analysis of the most successful conventional and DL-based algorithms proposed for each step. We also review existing open-source tools and public datasets that can help train DL models. To be more specific, we review exemplary traffic problems and mentioned requires steps for each problem. Besides, we investigate connections to the closely related research areas of drivers' cognition evaluation, Crowd-sourcing-based monitoring systems, Edge Computing in roadside infrastructures, ADS-equipped AVs, and highlight the missing gaps. Finally, we review commercial implementations of traffic monitoring systems, their future outlook, and open problems and remaining challenges for widespread use of such systems ‡ .

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“…These accidents (among others) have eroded public trust in AVs, and nearly 50% or more of the public have expressed their mistrust in AVs [6]. Current statistics indicate that perception and prediction errors were factors in over 40% of driver-related crashes between conventional vehicles [7]. However, a significant number of reported AV collisions are also the result of these errors [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

Malawade¹,

Yu²,

Hsu³

et al. 2021

Preprint

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In autonomous vehicles (AV), early warning systems rely on collision prediction to ensure occupant safety. However, state-of-the-art methods using deep convolutional networks either fail at modeling collisions or are too expensive/slow, making them less suitable for deployment on AV edge hardware. To address these limitations, we propose SG2VEC, a spatio-temporal scenegraph embedding methodology that uses Graph Neural Network (GNN) and Long Short-Term Memory (LSTM) layers to predict future collisions via visual scene perception. We demonstrate that SG2VEC predicts collisions 8.11% more accurately and 39.07% earlier than the state-of-the-art method on synthesized datasets, and 29.47% more accurately on a challenging real-world collision dataset. We also show that SG2VEC is better than the state-ofthe-art at transferring knowledge from synthetic datasets to realworld driving datasets. Finally, we demonstrate that SG2VEC performs inference 9.3x faster with an 88.0% smaller model, 32.4% less power, and 92.8% less energy than the state-of-the-art method on the industry-standard Nvidia DRIVE PX 2 platform, making it more suitable for implementation on the edge.

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What humanlike errors do autonomous vehicles need to avoid to maximize safety?

Cited by 59 publications

References 12 publications

Deep learning serves traffic safety analysis: A forward‐looking review

Deep learning serves traffic safety analysis: A forward‐looking review

Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review

Spatio-Temporal Scene-Graph Embedding for Autonomous Vehicle Collision Prediction

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