Integrating driving behavior and traffic context through signal symbolization

Yamazaki, Suguru; Miyajima, Chiyomi; Yurtsever, Ekim; Mori, Masataka; Hitomi, Kentarou; Egawa, Masumi

doi:10.1109/ivs.2016.7535455

Cited by 13 publications

(6 citation statements)

References 7 publications

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“…A framework for long term driver behavior prediction using a combination of a hybrid state system and HMM was introduced in [212]. Surrounding vehicle information was integrated with ego-behavior through a symbolization framework in [74], [209]. Detecting dangerous cut in maneuvers was achieved with an HMM framework that was trained on safe and dangerous data in [213].…”

Section: B Surrounding Driving Behavior Assessmentmentioning

confidence: 99%

“…An alternative approach is to assess the overall risk level of the driving scene separately, i.e outside the pipeline. Sensory inputs were fed into a risk inference framework in [74], [209] to detect unsafe lane change events using Hidden Markov Models (HMMs) and language models. Recently, a deep spatiotemporal network that infers the overall risk level of a driving scene was introduced in [197].…”

Section: A Risk and Uncertainty Assessmentmentioning

confidence: 99%

“…The vast majority of driving style recognition literature uses two or three classes. In [229]- [231] driving style was categorized into three and into two in [74], [209], [217], [218], [222]. Representing driving style in a continuous domain is uncommon, but there are some studies.…”

Section: Driving Style Recognitionmentioning

confidence: 99%

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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Automated driving systems (ADSs) promise a safe, comfortable and efficient driving experience. However, fatalities involving vehicles equipped with ADSs are on the rise. The full potential of ADSs cannot be realized unless the robustness of state-of-the-art improved further. This paper discusses unsolved problems and surveys the technical aspect of automated driving. Studies regarding present challenges, high-level system architectures, emerging methodologies and core functions: localization, mapping, perception, planning, and human machine interface, were thoroughly reviewed. Furthermore, the state-of-the-art was implemented on our own platform and various algorithms were compared in a real-world driving setting. The paper concludes with an overview of available datasets and tools for ADS

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Section: B Surrounding Driving Behavior Assessmentmentioning

confidence: 99%

Section: A Risk and Uncertainty Assessmentmentioning

confidence: 99%

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A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

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“…The dataset includes egovehicle signals such as steering and pedal operation, range information and frames captured by a front-facing camera close to the drivers' point of view. However, previous works on this dataset ignored the monocular camera footage and used ego-vehicle signals [16], [17].…”

Section: A Risk Studiesmentioning

confidence: 99%

Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer

Yurtsever

Liu

Lambert

et al. 2019

2019 IEEE Intelligent Transportation Systems Conference (ITSC)

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Advanced driver assistance and automated driving systems rely on risk estimation modules to predict and avoid dangerous situations. Current methods use expensive sensor setups and complex processing pipeline, limiting their availability and robustness. To address these issues, we introduce a novel deep learning based action recognition framework for classifying dangerous lane change behavior in short video clips captured by a monocular camera. We designed a deep spatiotemporal classification network that uses pre-trained state-of-the-art instance segmentation network Mask R-CNN as its spatial feature extractor for this task. The Long-Short Term Memory (LSTM) and shallower final classification layers of the proposed method were trained on a semi-naturalistic lane change dataset with annotated risk labels. A comprehensive comparison of state-of-the-art feature extractors was carried out to find the best network layout and training strategy. The best result, with a 0.937 AUC score, was obtained with the proposed network. Our code and trained models are available open-source 1 .

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“…Differing from previous research [9]- [12], [15] focusing on individual driver's behavior, we mainly concern how to generate an infinite number of new traffic scenarios from a handful of limited raw traffic data. Traffic scenarios could be generated by carefully and logically reshaping and cascading the basic compositions of traffic, termed as traffic primitive.…”

Section: Introductionmentioning

confidence: 99%

Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

Wang

Zhao

2018

IEEE Robot. Autom. Lett.

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Developing an automated vehicle, that can handle complicated driving scenarios and appropriately interact with other road users, requires the ability to semantically learn and understand driving environment, oftentimes, based on analyzing massive amounts of naturalistic driving data. An important paradigm that allows automated vehicles to both learn from human drivers and gain insights is understanding the principal compositions of the entire traffic, termed as traffic primitives. However, the exploding data growth presents a great challenge in extracting primitives from highdimensional time-series traffic data with various types of road users engaged. Therefore, automatically extracting primitives is becoming one of the cost-efficient ways to help autonomous vehicles understand and predict the complex traffic scenarios. In addition, the extracted primitives from raw data should 1) be appropriate for automated driving applications and also 2) be easily used to generate new traffic scenarios. However, existing literature does not provide a method to automatically learn these primitives from large-scale traffic data. The contribution of this paper has two manifolds. The first one is that we proposed a new framework to generate new traffic scenarios from a handful of limited traffic data. The second one is that we introduce a nonparametric Bayesian learning method -a sticky hierarchical Dirichlet process hidden Markov model -to automatically extract primitives from multidimensional traffic data without prior knowledge of the primitive settings. The developed method is then validated using one day of naturalistic driving data. Experiment results show that the nonparametric Bayesian learning method is able to extract primitives from traffic scenarios where both the binary and continuous events coexist.

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Integrating driving behavior and traffic context through signal symbolization

Cited by 13 publications

References 7 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

Risky Action Recognition in Lane Change Video Clips using Deep Spatiotemporal Networks with Segmentation Mask Transfer

Extracting Traffic Primitives Directly From Naturalistically Logged Data for Self-Driving Applications

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