Study Of Statistical Models For Route Prediction Algorithms In VANET

Vehicle-to-vehicle communication is a concept greatly studied during the past years. Vehicles equipped with devices capable of short-range wireless connectivity can form a particular mobile ad-hoc network, called a Vehicular Ad-hoc NETwork (or VANET). The users of a VANET, drivers or passengers, can be provided with useful information and with a wide range of interesting services. Route prediction is the missing piece in several proposed ideas for intelligent vehicles. In this paper, we are studying the algorithms that predict a vehicle's entire route as it is driven. Such predictions are useful for giving the driver warnings about upcoming traffic hazards or information about upcoming points of interest, including advertising. This paper describes the route Prediction algorithms using Markov Model, Hidden Markov Model (HMM), Variable order Markov model (VMM). Keywords: VANET, MANET, ITs, GPS, HMM, VMM, PST

Integrating Multiple Alarms & Driver Situation Awareness

This study addresses this gap in CAS and intelligent alarm research by examining whether or not a single master alarm warning versus multiple warnings for the different collision warning systems conveys adequate information to the drivers. Intelligent driver warning systems signaling impending frontal and rear collisions, as well as unintentional lane departures were used in this experiment, and all the warnings were presented to drivers through the auditory channel only. We investigated two critical research questions in this study: 1. Do multiple intelligent alarms as opposed to a single master alarm affect drivers’ recognition, performance, and action when they experience a likely imminent collision and unintentional lane departure? 2. Is driver performance and overall situation awareness under the two different alarm alerting schemes affected by reliabilities of the warning systems?Prepared For Ford Motor Compan

Real-time prevention tool integrating volatility and environmental impacts

In Europe, the number of road traffic deaths and injuries is still far too high and the European Union is committed in improving road safety and move closer to the target of approaching zero road fatalities by 2050. For that purpose, new strategies based on the Safe System approach to preventing deaths and serious injuries for all road users should be developed. Road transport is a major source of pollutant emissions. In particular, it is responsible for the emission of harmful pollutants such as nitrogen oxides (NOx) and carbon dioxide (CO2), which has serious impacts in global warming [1]. It is known that driver behavior can play a key role in what concerns road crashes and pollutant emissions. Such impacts increase when associated to aggressive behavior, experiencing high and extreme levels of fuel consumption, speed and acceleration. A deep understanding of driver behavior should be an important step to improve road safety. Various studies have been conducted to identify driver’s behavior under many contexts such as, traffic, roadway and weather conditions. An issue that has not been so explored is an analysis of drivers’ volatility [2-3]. Volatility can be defined as the extent of variations in driving, which can be characterized by accelerations/braking, lane change and also unusual high speed for roadways conditions. Therefore, particular attention should be given to developing preventive tools, anticipating dangerous situations and warning the driver that may be efficient solutions to avoid an occurrence. In [4], an advisory system was developed on a driver’s simulator to warning the driver. However, there is no preventive tool in the literature that integrates volatility and environmental impacts. The main objective of this work is to develop a decision support system to evaluate driver volatility and provide instantaneous and integrated information on safety and emission impacts to the driver. To validate our application, we used real traffic, dynamic and on-road emissions data collected from probe vehicles on two highways of different specificities (e.g., slope, relief and traffic volumes). A simulation-based approach through Vissim COM API using Matlab was constructed in order to give to the driver warnings regarding safety and emissions. Markov Decision Process (MDP) was used to support the decision on safety and the Vehicle Specific Power (VSP) methodology was used for estimating pollutant emissions.publishe

EFRC Bulletin 81 December 2005

EFRC's regular Bulletin with updates from the Organic Advisory Servic

Forward collision warning modality and content: a summary of human factors studies

The report summarizes a nonexhaustive sample of 17 studies covering 27 experiments on human factors and forward-collision warnings. Subject samples ranged from 11 to 260 (median=30). Twenty-three experiments were conducted using driving simulators; 4 were on test tracks. Typically subjects followed a lead vehicle that braked abruptly, triggering audio, visual, tactile, or combined warnings. Response/reaction time was reported as a dependent measure in 18 of the 27 experiments, the number of crashes in 8, distance headway (gap) in 3, perceived urgency in 7 (both by the same authors), perceived annoyance in 11, and probability of warning recall in 1. Providing a warning leads to a more desired outcome. Response/reaction times were briefer in 9 of the 9 studies that considered this and all 4 of the studies that examined crashes reported fewer crashes with warnings. Warnings 4 to 10 dB above the background level led to the best performance, but only one study systematically varied warning intensity. Of the combinations explored, multimodal warnings tended to lead to better performance than unimodal warnings, though none of them considered seat-belt-pretensioner activation, an effective way to reduce crash injuries. Studies could be improved by the use of consistent crash scenarios, defined measures, predictions of performance, and including older drivers in test samples.Nissan Technical Center North Americahttp://deepblue.lib.umich.edu/bitstream/2027.42/134038/1/103247.pdf-1Description of 103247.pdf : Final repor

Two Tree Trimmers Die When Struck by Errant Semi Tractor-Trailer

One spring morning in 2010, three male tree trimmers set up a work site in a highway intersection at the base of a mountain. As one tree trimmer removed equipment from the truck, two other tree trimmers, 21- and 32-years old, posted signage in the intersection. The two tree trimmers were located on the shoulder of the highway. A semi tractor-trailer was driving down the mountain toward the intersection, when its brakes failed. To avoid hitting vehicles in front of the semi, the driver steered to the left, crossed the intersection, striking both tree trimmers with the semi tractor-trailer. Both tree trimmers died at the scene. To prevent future occurrences of similar incidents, the following recommendations have been made: Recommendation No. 1: Roadside inspectors should prevent commercial drivers from continuing to operate a semi tractor-trailer when taken out-of-service due to inspection. Recommendation No. 2: Commercial drivers should inform employers of roadside inspection results. Recommendation No. 3: Commercial carriers should perform random verification checks of driver motor vehicle records. Recommendation No. 4: A certified annual commercial vehicle inspection program should be established. Recommendation No. 5: Employers should require proof that operators have performed daily safety checks on the semi tractor-trailer prior to operation. The following separate recommendation is being made: Recommendation No. 6: The toll-free number to report illegal and/ or dangerous commercial driver activity to the Federal Motor Carrier Administration should be advertised in rest areas and truck stops

Transport Use Case Trials of 5G-HEART Project

5G-HEART (5G HEalth AquacultuRe and Transport validation trials) project focuses on realising 5G trials and validating 5G Key Performance Indicators (KPIs) on the vital vertical use-cases of healthcare, transport and aquaculture. In the health area, 5G-HEART validates pillcams for automatic detection in screening of colon cancer and vital-sign patches with advanced geo-localization as well as 5G Augmented/Virtual Reality (AR/VR) paramedic services. In the transport area, 5G-HEART validates autonomous/ assisted/ remote driving and vehicle data services. In the aquaculture area, 5G-HEART validates 5G-based fish farm monitoring systems. Among them, transport sector connected with fifth generation (5G) mobile communications systems will drive transformational changes while bringing social, economic and industrial benefits to the economies that take the lead in its adoption. 5G-enabled industry digitisation for automotive and public transport represents 10% and 8% of the overall $1.3T market in 2026, respectively. In this paper, transport use case trials of 5G-HEART project are introduced