Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning

Liu, Hailong; Taniguchi, Tadahiro; Tanaka, Yusuke; Takenaka, Kazuhito; Bando, Takashi

doi:10.1109/tits.2017.2649541

Cited by 122 publications

(80 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Another autoencoder network was used to extract road-type specific driving features [240]. Similarly, driving behavior was encoded in a 3-channel RGB space with a deep sparse autoencoder to visualize individual driving styles [241]. A successful integration of driving style recognition into a real world ADS pipeline is not reported yet.…”

Section: Driving Style Recognitionmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

View full text Add to dashboard Cite

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

show abstract

Section: Driving Style Recognitionmentioning

confidence: 99%

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

et al. 2020

View full text Add to dashboard Cite

show abstract

“…We calculated the PSD of three EEG bands namely theta (4-7 Hz), alpha (7-13 Hz) and Beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30) Hz) for all the EEG channels. The choice of these three specific EEG bands was made since they are the most commonly used bands and thought to carry a lot of information about human cognition.…”

Section: Features Based On Deep Learningmentioning

confidence: 99%

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Siddharth

Trivedi

2020

Brain Sciences

View full text Add to dashboard Cite

Automobiles for our roadways are increasingly utilizing advanced driver assistance systems. These changes require us to develop novel perception systems not only for accurately understanding the situations and context of these vehicles, but also to understand the awareness of the driver in differentiating between safe and critical situations. The research presented in this paper focused on this specific problem. Even after the development of wearable and compact multi-modal bio-sensing systems in recent years, their application in driver awareness context has been scarcely explored. The capability of simultaneously recording different kinds of bio-sensing data in addition to traditionally used computer vision systems provide exciting opportunities to explore the limitations of these modalities. In this work, we explore the applications of three different bio-sensing modalities namely electroencephalogram (EEG), photoplethysmogram (PPG) and galvanic skin response (GSR) along with a camera-based vision system in driver awareness context. We assess the information from these sensors independently and together using both signal processing and deep learning tools. We show that our methods outperform previously reported studies in the context of monitoring driver awareness and detecting hazardous/non-hazardous situations for short time scales of two-seconds. We verify our methods by collecting user data on twelve subjects for two real-world driving datasets among which one is publicly available (KITTI dataset) and one that we collect ourselves (LISA dataset) with the vehicle being driven in an autonomous mode. This work presents an exhaustive evaluation of multiple sensor modalities on two different datasets for attention monitoring and hazardous events classification.

show abstract

“…First, BDA can help to provide public with useful transportation information, such as traffic jams, road blockage due to an event and malfunctioning traffic infrastructures. Second, BDA also offers drivers real-time information about driving safety, such as collision avoidance, turn assistant and pedestrian crossing information [29]. • Enhancing ride quality.…”

Section: Vehicular Networkmentioning

confidence: 99%

Big Data Analytics for Large-scale Wireless Networks

et al. 2019

View full text Add to dashboard Cite

The wide proliferation of various wireless communication systems and wireless devices has led to the arrival of big data era in large scale wireless networks. Big data of large scale wireless networks has the key features of wide variety, high volume, real-time velocity and huge value leading to the unique research challenges that are different from existing computing systems. In this paper, we present a survey of the state-of-art big data analytics (BDA) approaches for large scale wireless networks. In particular, we categorize the life cycle of BDA into four consecutive stages: Data Acquisition, Data Preprocessing, Data Storage and Data Analytics. We then present a detailed survey of the technical solutions to the challenges in BDA for large scale wireless networks according to each stage in the life cycle of BDA. Moreover, we discuss the open research issues and outline the future directions in this promising area.

show abstract

Visualization of Driving Behavior Based on Hidden Feature Extraction by Using Deep Learning

Cited by 122 publications

References 22 publications

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

A Survey of Autonomous Driving: Common Practices and Emerging Technologies

On Assessing Driver Awareness of Situational Criticalities: Multi-modal Bio-Sensing and Vision-Based Analysis, Evaluations, and Insights

Big Data Analytics for Large-scale Wireless Networks

Contact Info

Product

Resources

About