Traffic jam driving with NMV avoidance

Milanés, Vicente; Alonso, L.; Villagrá, Jorge; Godoy, Jorge; Pedro, Teresa de; Oria, J.P.

doi:10.1016/j.ymssp.2012.04.008

Cited by 9 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In order to improve the safety of driving in urban environments. These systems provide warning signals for the driver in the case that any unexpected traffic circumstance is detected, the system can act to alert the driver of collision risk and the system will be capable of autonomously avoiding collisions in traffic jam then provide incident reports based on V2V [7,31,47]. The system estimates an accident duration based on the time passed since the accident and provides an alternative route for vehicles approaching the accident zone to decrease waiting and travel times and avoid traffic [70].…”

Section: Safety Applicationsmentioning

confidence: 99%

A review on vehicle to vehicle communication system applications

Ameen¹,

Mahamad²,

Saon³

et al. 2020

IJEECS

View full text Add to dashboard Cite

<span>The field of automated vehicle technology is developing rapidly developing. While it is likely to be many years before self-driving cars are commercially viable and used in a wide range of conditions by the general public, technological advances are speeding along the automated technology continuum towards this destination. Automated vehicle technologies troth with significant social benefits such as reduced injuries and deaths, increased road efficiency, mobility. Automated vehicles can improve traffic safety, balance traffic flows, maximize road usage by offering driver warnings and/or assuming vehicle control in dangerous situations, as well as provide motorists with the best end-to-end transportation experience and reduce emissions, which are the most important goals of modern smart traffic control infrastructures. Exchanging data and integration of such systems with Vehicle-to-Vehicle (V2V) may be a keystone to successful readying of vehicular ad-hoc networks (VANETs) and will simply be the following step of this evolution, with dynamic period of time data exchange between all the players of the traffic dominant system and fostering cooperative urban quality. One of the applications of this concept is to provide vehicles and roads with the ability to make road time more enjoyable and also to make roads safer. These applications are typical examples of what an Intelligent Transportation System (ITS) is called, whose objective is to improve security by using new information and communication technologies (NTIC). In this paper, we will focus on the study of the main component in ITS systems and present a review of the major V2V benefits related to driver safety by focusing primarily on the recent developments of these systems.</span>

show abstract

Section: Safety Applicationsmentioning

confidence: 99%

A review on vehicle to vehicle communication system applications

Ameen¹,

Mahamad²,

Saon³

et al. 2020

IJEECS

View full text Add to dashboard Cite

show abstract

“…The factor cas 2 [,yw(fc)] avoids big accelerations or decelerations when the front wheels are rotated.…”

Section: Control Systemmentioning

confidence: 99%

Self-tuning PID controller for autonomous car tracking in urban traffic

Alonso

Pérez-Oria

Al‐Hadithi

et al. 2013

2013 17th International Conference on System Theory, Control and Computing (ICSTCC)

Self Cite

View full text Add to dashboard Cite

Abstract-In this paper an on line self-tuned PID controller is proposed for the control of a car whose goal is to follow another one, at distances and speeds typical in urban traffic. The bestknown tuning mechanism is perhaps the MIT rule, due to its ease of implementation. However, as it is well known, this method does not guarantee the stability of the system, providing good results only for constant or slowly varying reference signals and in the absence of noise, which are unrealistic conditions. When the reference input varies with an appreciable rate or in presence of noise, eventually it could result in system instability. In this paper an alternative method is proposed that significantly improves the robustness of the system for varying inputs or in the presence of noise, as demonstrated by simulation.

show abstract

“…Whether or not the collision is avoidable because it determines the vehicle type of action should automatically take [9]. The next step is to develop systems capable of warning the driver to avoid a potential collision [10]. Road users must be notified of potentially dangerous situations to react on time and appropriately.…”

Section: Introductionmentioning

confidence: 99%

Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

et al. 2021

View full text Add to dashboard Cite

Driver behavior is a determining factor in more than 90% of road accidents. Previous research regarding the relationship between speeding behavior and crashes suggests that drivers who engage in frequent and extreme speeding behavior are overinvolved in crashes. Consequently, there is a significant benefit in identifying drivers who engage in unsafe driving practices to enhance road safety. The proposed method uses continuously logged driving data to collect vehicle operation information, including vehicle speed, engine revolutions per minute (RPM), throttle position, and calculated engine load via the on-board diagnostics (OBD) interface. Then the proposed method makes use of severity stratification of acceleration to create a driving behavior classification model to determine whether the current driving behavior belongs to safe driving or not. The safe driving behavior is characterized by an acceleration value that ranges from about ±2 m/s2. The risk of collision starts from ±4 m/s2, which represents in this study the aggressive drivers. By measuring the in-vehicle accelerations, it is possible to categorize the driving behavior into four main classes based on real-time experiments: safe drivers, normal, aggressive, and dangerous drivers. Subsequently, the driver’s characteristics derived from the driver model are embedded into the advanced driver assistance systems. When the vehicle is in a risk situation, the system based on nRF24L01 + power amplifier/low noise amplifier PA/LNA, global positioning system GPS, and OBD-II passes a signal to the driver using a dedicated liquid-crystal display LCD and light signal. Experimental results show the correctness of the proposed driving behavior analysis method can achieve an average of 90% accuracy rate in various driving scenarios.

show abstract

Traffic jam driving with NMV avoidance

Cited by 9 publications

References 17 publications

A review on vehicle to vehicle communication system applications

A review on vehicle to vehicle communication system applications

Self-tuning PID controller for autonomous car tracking in urban traffic

Identification of Driving Safety Profiles in Vehicle to Vehicle Communication System Based on Vehicle OBD Information

Contact Info

Product

Resources

About