Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Terán, José; Navarro, Loraine; M., Christian G. Quintero; Pardo, Mauricio

doi:10.3390/s20061763

Cited by 14 publications

(9 citation statements)

References 22 publications

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“…A mathematical model was embedded in a neural network to improve classification [26]. This particular research theme was then expanded to include intelligent driving assistance for autonomous vehicles, with decisions not just for changing lanes but also to avoid risk with or without autopilot mode activated [27,28,29]. Overall, there are more studies regarding driving behavior modeling than driver behavior modeling.…”

Section: Vehicle Control System and Modelmentioning

confidence: 99%

Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach

Zulkarnain¹,

Putri²

2021

Heliyon

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Intelligent Transportation Systems (ITS) is not a new concept. Notably, ITS has been cited in various journal articles and proceedings papers around the world, and it has become increasingly popular. Additionally, ITS involves multidisciplinary science. The growing number of journal articles makes ITS reviews complicated, and research gaps can be difficult to identify. The existing software for systematic reviews still relies on highly laborious tasks, manual reading, and a homogeneous dataset of research articles. This study proposes a framework that can address these issues, return a comprehensive systematic review of ITS, and promote efficient systematic reviews. The proposed framework consists of Natural Language Processing (NLP) methods i.e., Named Entity Recognition (NER), Latent Dirichlet Allocation (LDA), and word embedding (continuous skip-gram). It enables this study to explore the context of research articles and their overall interpretation to determine and define the directions of knowledge growth and ITS development. The framework can systematically separate unrelated documents and simplify the review process for large dataset. To our knowledge, compared to prior research regarding systematic review of ITS, this study offers more thorough review.

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Section: Vehicle Control System and Modelmentioning

confidence: 99%

Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach

Zulkarnain¹,

Putri²

2021

Heliyon

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“… Path planning (1): [ 36 ] Smart regenerative braking systems for electric vehicles (2): [ 37 , 38 ]. Physical intelligence in sensors and sensing (3): [ 39 , 40 , 41 ] Driver assistance systems and automatic vehicle operation (3) Advanced driver assistance systems (1): [ 42 ]. Automatic parking of road vehicles (1): [ 43 ].…”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

“…Driver assistance systems and automatic vehicle operation (3) Advanced driver assistance systems (1): [ 42 ]. Automatic parking of road vehicles (1): [ 43 ].…”

Section: Special Issue On Intelligent Vehiclesmentioning

confidence: 99%

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Sensors and Sensing for Intelligent Vehicles

Llorca

Daza

Hernández

et al. 2020

Sensors

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Over the past decades, both industry and academy have made enormous advancements in the field of intelligent vehicles, and a considerable number of prototypes are now driving our roads, railways, air and sea autonomously. However, there is still a long way to go before a widespread adoption. Among all the scientific and technical problems to be solved by intelligent vehicles, the ability to perceive, interpret, and fully understand the operational environment, as well as to infer future states and potential hazards, represent the most difficult and complex tasks, being probably the main bottlenecks that the scientific community and industry must solve in the coming years to ensure the safe and efficient operation of the vehicles (and, therefore, their future adoption). The great complexity and the almost infinite variety of possible scenarios in which an intelligent vehicle must operate, raise the problem of perception as an "endless" issue that will always be ongoing. As a humble contribution to the advancement of vehicles endowed with intelligence, we organized the Special Issue on Intelligent Vehicles. This work offers a complete analysis of all the mansucripts published, and presents the main conclusions drawn.

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“…Some of the input parameters for monitoring driver behavior are gravity, throttle, speed, revolutions per minute, and acceleration. The types of driver behavior are normal, aggressive, distracted, drowsy, and drunk driving [13,14]. This information is measured by sensors that are placed on the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

et al. 2021

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The emerging technology of internet of connected vehicles (IoCV) introduced many new solutions for accident prevention and traffic safety by monitoring the behavior of drivers. In addition, monitoring drivers’ behavior to reduce accidents has attracted considerable attention from industry and academic researchers in recent years. However, there are still many issues that have not been addressed due to the lack of feature extraction. To this end, in this paper, we propose the multimodal driver analysis internet of connected vehicles (MODAL-IoCV) approach for analyzing drivers’ behavior using a deep learning method. This approach includes three consecutive phases. In the first phase, the hidden Markov model (HMM) is proposed to predict vehicle motion and lane changes. In the second phase, SqueezeNet is proposed to perform feature extraction from these classes. Lastly, in the final phase, tri-agent-based soft actor critic (TA-SAC) is proposed for recommendation and route planning, in which each driver is precisely handled by an edge node for personalized assistance. Finally, detailed experimental results prove that our proposed MODAL-IoCV method can achieve high performance in terms of latency, accuracy, false alarm rate, and motion prediction error compared to existing works.

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Intelligent Driving Assistant Based on Road Accident Risk Map Analysis and Vehicle Telemetry

Cited by 14 publications

References 22 publications

Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach

Intelligent transportation systems (ITS): A systematic review using a Natural Language Processing (NLP) approach

Sensors and Sensing for Intelligent Vehicles

Multimodal Feature-Assisted Continuous Driver Behavior Analysis and Solving for Edge-Enabled Internet of Connected Vehicles Using Deep Learning

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