Driving style indicator using UDRIVE NDS data

Guyonvarch, Laurette; Hermitte, Thierry; Duvivier, F.; Val, Clement; Guillaume, Anne

doi:10.1080/15389588.2018.1426920

Cited by 7 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…UDRIVE helped researchers define a driving style indicator based on secondary task engagement (e.g., phone usage), as well as analyzing regional differences among different drivers. [61], [62].…”

Section: Naturalistic Driving Studiesmentioning

confidence: 99%

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

et al. 2021

View full text Add to dashboard Cite

Effective shared autonomy requires a clear understanding of driver's behavior, which is governed by multiple psychophysiological and environmental variables. Disentangling this intricate web of interactions requires understanding the driver's state and behaviors in different real-world scenarios, longitudinally. Naturalistic Driving Studies (NDS) have shown to be an effective approach to understanding the driver's state and behavior in real-world scenarios. However, due to the lack of technological and computing capabilities, former NDS only focused on vision-based approaches, ignoring important psychophysiological factors such as cognition and emotion. The main objective of this paper is to introduce HARMONY, a human-centered multimodal naturalistic driving study, where driver's behaviors and states are monitored through (1) in-cabin and outside video streams (2) physiological signals including driver's heart rate and hand acceleration (IMU data), (3) ambient noise, light, and the vehicle's GPS location, and (4) music logs, including song features such as tempo. HARMONY is the first study that collects long-term naturalistic facial, physiological, and environmental data simultaneously. This paper summarizes HARMONY's goals, framework design, data collection and analysis, and the on-going and future research efforts. Through a presented case study, we first demonstrate the importance of longitudinal driver state sensing through using Kernel Density Estimation Methods. Second, we leverage the application of Bayesian Change Point detection methods to demonstrate how we can identify driver behaviors and responses to the environmental conditions by fusing psychophysiological information with features extracted from video streams.INDEX TERMS Naturalistic driving study, physiological sensing,driver state detection, shared-autonomy, contextual awareness, human-in-the-loop systems

show abstract

Section: Naturalistic Driving Studiesmentioning

confidence: 99%

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

et al. 2021

View full text Add to dashboard Cite

show abstract

“…Therefore, data pre-processing tasks but not limited to noise filtering or denoising, are required before feeding the high dimensional data to the model training stage. Noise isolation from the driving data can be done through low-pass filters such as Butterworth filter [136], median filter [137], wavelet filter [138], Kalman filter [139], and moving average filter, etc. The other pre-processing task is termed segmentation.…”

Section: Data-driven Behavior Modelsmentioning

confidence: 99%

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Negash

Yang

2023

IEEE Access

View full text Add to dashboard Cite

Driver behavior models have been used as input to self-coaching, accident prevention studies, and developing driver-assisting systems. In recent years, driver behavior recognition has revolutionized autonomous vehicles (AVs) and traffic management studies. This comprehensive survey provides an up-todate review of the different driver behavior models and modeling approaches. In heterogeneous streets where humans and autonomous vehicles operate simultaneously, predicting the intent and action of human drivers is crucial for AVs with the help of wireless communication and artificial intelligence (AI) technologies. Therefore, the review also summarizes the applications of driver behavior modeling (DBM) for effective behavior recognition and human-like AV driving. Moreover, the review also covers the application of DBM in capturing behaviors of complex dynamic driving tasks. In this review, we solely cover car-following (CF) and lane-changing (LC) maneuvers.INDEX TERMS Driver behavior modeling (DBM), autonomous vehicles, behavior recognition, human-like, car-following, lane-changing, perception, decision-making, data-driven, vehicle control. I. INTRODUCTIONIn 2018, the US Department of Transportation reported 6,734,000 motor vehicle-related accidents. 1 Among these accidents, there were 2.71 million injuries and 36,560 fatalities. These deaths and injuries also increased in the years 2019 and 2020. Earlier in 2015, specifically in the US, 80 − 94% of crashes were due to driver-related errors, and 33% were due to the wrong prediction of other drivers [1]. The statistics of accidents caused by human driver-related errors were also reported in [2] and [3].The accidents and their behind reasons will lead us back to human drivers with rich and diverse behaviors. According to Michon [4], a human as a problem solver can be a road user, a transportation consumer, a social agent, and a psycho-biological organism. The issues to be solved are controlling a vehicle, trip-making, communication, and satisfying the basic driving necessity. A human driver can have unique traits towards vehicle controlThe associate editor coordinating the review of this manuscript and approving it for publication was Divanilson Rodrigo Campelo .

show abstract

“…The acquisition of driving behavior, the driver’s manipulation of the vehicle, has generally required the addition of specialized sensor equipment. For example, Australian Naturalistic Driving Research [ 11 ] and the European Naturalistic Driving Study UDRIVE [ 12 , 13 ] installed numerous customized sensing devices to capture information, including throttle and brake pedal positions and steering wheel angles. Similarly, more sensor devices have been employed for the collection of driving scene data, such as combinations of Lidar, multiple types of cameras, and global navigation satellite systems.…”

Section: Introductionmentioning

confidence: 99%

Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm

Gao

Sun

et al. 2022

Sensors

View full text Add to dashboard Cite

Big data already covers intelligent vehicles and is driving the autonomous driving industry’s transformation. However, the large amounts of driving data generated will result in complex issues and a huge workload for the test and verification processes of an autonomous driving system. Only effective and precise data extraction and recording aimed at the challenges of low efficiency, poor quality, and a long-time limit for traditional data acquisition can substantially reduce the algorithm development cycle. Based on the premise of driver-dominated vehicle movement, the virtual decision-making of autonomous driving systems under the accompanying state was considered as a reference. Based on a dynamic time warping algorithm and forming a data filtering approach under a dynamic time window, an automatic trigger recording control model for human-vehicle difference feature data was suggested. In this method, the data dimension was minimized, and the efficiency of the data mining was improved. The experimental findings showed that the suggested model decreased recorded invalid data by 75.35% on average and saved about 2.65 TB of data storage space per hour. Compared with industrial-grade methods, it saves an average of 307 GB of storage space per hour.

show abstract

Driving style indicator using UDRIVE NDS data

Abstract: DSI is a new parameter that will be used to define clusters of drivers and study variation of driving parameters in each class during selected events (SCE, secondary task, etc.) in the UDRIVE project.

Cited by 7 publications

References 0 publications

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

HARMONY: A Human-Centered Multimodal Driving Study in the Wild

Driver Behavior Modeling Toward Autonomous Vehicles: Comprehensive Review

Data Filtering Method for Intelligent Vehicle Shared Autonomy Based on a Dynamic Time Warping Algorithm

Contact Info

Product

Resources

About