scholarly journals SenSafe: A Smartphone-Based Traffic Safety Framework by Sensing Vehicle and Pedestrian Behaviors

2016 ◽  
Vol 2016 ◽  
pp. 1-13 ◽  
Author(s):  
Zhenyu Liu ◽  
Mengfei Wu ◽  
Konglin Zhu ◽  
Lin Zhang
Keyword(s):  
Compressed Sensing ◽  
Real Time ◽  
Traffic Safety ◽  
Traffic Accident ◽  
Short Range ◽  
Estimation Algorithm ◽  
Detection Mechanism ◽  
Sensing Data ◽  
Driving Behaviors ◽  
Dedicated Short Range Communication

Traffic accident involving vehicles is one of the most serious problems in the transportation system nowadays. How to detect dangerous steering and then alarm drivers in real time is a problem. What is more, walking while using smartphones makes pedestrian more susceptible to various risks. Although dedicated short range communication (DSRC) provides the way for safety communications, most of vehicles have not been deployed with DSRC components. Even worse, DSRC is not supported by the smartphones for vehicle-to-pedestrian (V2P) communication. In this paper, a smartphone-based framework named SenSafe is developed to improve the traffic safety. SenSafe is a framework which only utilizes the smartphone to sense the surrounding events and provides alerts to drivers. Smartphone-based driving behaviors detection mechanism is developed inside the framework to discover various steering behaviors. Besides, the Wi-Fi association and authentication overhead is reduced to broadcast the compressed sensing data using the Wi-Fi beacon to inform the drivers of the surroundings. Furthermore, a collision estimation algorithm is designed to issue appropriate warnings. Finally, an Android-based implementation of SenSafe framework has been achieved to demonstrate the application reliability in real environments.

Traffic accident and emission reduction through intermittent release measures for heavy fog weather

2015 ◽  
Vol 29 (25) ◽  
pp. 1550148 ◽  
Author(s):  
Jing Shi ◽  
Jin-Hua Tan
Keyword(s):  
Traffic Safety ◽  
Emission Reduction ◽  
Traffic Accident ◽  
Traffic Accidents ◽  
Driving Simulator ◽  
Emission Characteristics ◽  
Driving Behaviors ◽  
Ca Model ◽  
Reduce Emissions

Heavy fog weather can increase traffic accidents and lead to freeway closures which result in delays. This paper aims at exploring traffic accident and emission characteristics in heavy fog, as well as freeway intermittent release measures for heavy fog weather. A driving simulator experiment is conducted for obtaining driving behaviors in heavy fog. By proposing a multi-cell cellular automaton (CA) model based on the experimental data, the role of intermittent release measures on the reduction of traffic accidents and CO emissions is studied. The results show that, affected by heavy fog, when cellular occupancy [Formula: see text], the probability of traffic accidents is much higher; and CO emissions increase significantly when [Formula: see text]. After an intermittent release measure is applied, the probability of traffic accidents and level of CO emissions become reasonable. Obviously, the measure can enhance traffic safety and reduce emissions.

scholarly journals A Fast Weakly-Coupled Double-Layer ESKF Attitude Estimation Algorithm and Application

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1465
Author(s):  
Yue Yang ◽  
Xiaoxiong Liu ◽  
Weiguo Zhang ◽  
Xuhang Liu ◽  
Yicong Guo
Keyword(s):  
Real Time ◽  
Double Layer ◽  
Short Range ◽  
Estimation Algorithm ◽  
Attitude Estimation ◽  
Sensor Data ◽  
Test Environment ◽  
Attitude Error ◽  
Weakly Coupled ◽  
Solution Accuracy

Aimed at the problem of small unmanned aerial vehicle (UAV) attitude solution accuracy and real-time performance in short-range navigation flight, in this paper, we propose a fast weakly-coupled double-layer error-state Kalman filter (DL-ESKF) attitude estimation algorithm. Considering the application of short-range navigation, we designed an improved attitude error model for low-cost gyroscope/accelerometer/magnetometer devices. In addition, we reasonably simplified certain factors that affect the attitude solution to reduce the filtering calculation burden. For the data coupling phenomenon caused by the different sampling frequencies of the attitude sensor data in the filtering process, we designed a new attitude algorithm combined with the ESKF and hierarchical filter. The first layer of filters used an accelerometer and the second layer used a magnetometer to correct the attitude error. We also built an offline and real-time test platform to verify the performance of the proposed algorithm in a simulation and flight test environment compared with the classic attitude algorithms. The experimental results demonstrated that the proposed algorithm not only improved the attitude solution accuracy and stability but also reduced the filter running time.

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