Intelligent driving assistant based on accident risk maps analysis and intelligent driving diagnosis

Quintero, M. Christian G.; Cuervo, P. Andres C.

doi:10.1109/ivs.2017.7995832

Cited by 10 publications

(4 citation statements)

References 5 publications

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“…Although the authors refer to the importance of these parameters, the proposed system does not provide for their use or any way to detect them. In [9], the authors use artificial neural networks to propose an intelligent recommendation system for drivers, especially in areas where there is a greater risk of accidents. The system is based on the recognition of the outer area based on maps, rather than on driver monitoring.…”

Section: Literature Review 21 Review Of Adasmentioning

confidence: 99%

User Preferences in the Design of Advanced Driver Assistance Systems

et al. 2021

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The transport network and mobility aspects are constantly changing, and major changes are expected in the coming years in terms of safety and sustainability purposes. In this paper, we present the main conclusions and analysis of data collected from a survey of drivers in Spain and Portugal regarding user preferences, highlighting the main functionalities and behavior that an advanced driver assistance system must have in order to grant it special importance on the road to prevent accidents and also to enable drivers to have a pleasant journey. Based on the results obtained from the survey, we developed and present a working prototype for an advanced driver assistance system (ADAS), its architecture and rules systems that allowed us to create and test some scenarios in a real environment.

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Section: Literature Review 21 Review Of Adasmentioning

confidence: 99%

User Preferences in the Design of Advanced Driver Assistance Systems

et al. 2021

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“…are the main advantages offered by them. In addition, they facilitate the study of new approaches in cases where implementing them directly in a real vehicle and a real environment can cause highly risky situations [19,[25][26][27][28][29]. However, no matter how sophisticated the simulators are, they cannot provide all the physical aspects related to the actual driving process.…”

Section: Introductionmentioning

confidence: 99%

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

Terán

Navarro

et al. 2020

Sensors

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Through the application of intelligent systems in driver assistance systems, the experience of traveling by road has become much more comfortable and safe. In this sense, this paper then reports the development of an intelligent driving assistant, based on vehicle telemetry and road accident risk map analysis, whose responsibility is to alert the driver in order to avoid risky situations that may cause traffic accidents. In performance evaluations using real cars in a real environment, the on-board intelligent assistant reproduced real-time audio-visual alerts according to information obtained from both telemetry and road accident risk map analysis. As a result, an intelligent assistance agent based on fuzzy reasoning was obtained, which supported the driver correctly in real-time according to the telemetry data, the vehicle environment and the principles of secure driving practices and transportation regulation laws. Experimental results and conclusions emphasizing the advantages of the proposed intelligent driving assistant in the improvement of the driving task are presented.

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“…The ICVs adopt advanced sensing, control, and decision-making technologies. It aims to achieve information exchange among vehicles and the goal of safer, more stable and faster driving in full-condition [3]. Under the support of vehicle-mounted platforms and infrastructure hardware, the ICVs have already achieved applications, including road and related on-road marks identification [4]- [6], intelligent decisionmaking [7], [8], positioning [9], [10], and navigation [11].…”

Section: Introductionmentioning

confidence: 99%

“…Second, to test and evaluate the vehicle performance of our presented HVCD system, we propose three evaluation criterions, including vehicle safety based on vehicle relative position on road, vehicle stability based on linear and angle acceleration/deceleration, and vehicle rapidity based on speed. These three evaluation criterions are of fundamental requirements to a driving system, and are also of significantly importance for all kinds of driving applications [3] As the ability of vehicle intelligence to discriminate against human behavior is still limited [34], [35], evaluation criterions set manually is reasonable. Third, with these evaluation criterions, we tested our proposed semi-physical HVCD simulation system in a multi-condition road map by asking the participated subjects to drive the simulated vehicle from a particular start point to a destination.…”

Section: Introductionmentioning

confidence: 99%

Design and Performance Evaluation of a Simple Semi-Physical Human-Vehicle Collaborative Driving Simulation System

Duan

2019

IEEE Access

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Human-vehicle collaborative driving (HVCD) has a novel concept of an intermediate stage from the development of a normal smart vehicle to self-driving vehicle, requires the vehicle's decisionmaking authority equivalent to the human driver's operation. As the self-driving vehicle cannot achieve full-condition driving without human driver yet and still have a long time to go, the collaborative driving can exploit both machine and human specialties, which requires human and vehicle intelligence to control the vehicle simultaneously and work together. However, there were a few studies focusing on the HVCD that experimental environment and evaluation methods are rarely discussed, even under the simulation conditions, which limits the development of collaborative driving. To bridge the significant gaps, this paper designed simple semi-physical simulation system architecture for the HVCD. First, the simple designed system can achieve basic functions, including human control input as the normal driver, vehicle intelligence with local and global sensor data process and decision fusion. The presented system can use and process this multisource information to achieve the HVCD in the simulated environment. Second, the evaluation method based on vehicle driving safety, stability, and rapidity is proposed and discussed, which matters most for system performance test and feasibility verification. In addition, experiments with different decision fusion conditions were presented and the experimental results show that the proposed simple semi-physical HVCD simulation system can work smoothly and reflect the practical scenario. Furthermore, the performance of different fusion weights experiments shows the advantages of both human driver and vehicle intelligence, which provides a reference for the HVCD related research improvement. INDEX TERMS Human-vehicle collaborative driving, simulation system design, vehicle driving performance evaluation.

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Intelligent driving assistant based on accident risk maps analysis and intelligent driving diagnosis

Cited by 10 publications

References 5 publications

User Preferences in the Design of Advanced Driver Assistance Systems

User Preferences in the Design of Advanced Driver Assistance Systems

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

Design and Performance Evaluation of a Simple Semi-Physical Human-Vehicle Collaborative Driving Simulation System

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