- Python record data from globalsat bu 353 s4 usb gps receiver apk#
- Python record data from globalsat bu 353 s4 usb gps receiver serial#
- Python record data from globalsat bu 353 s4 usb gps receiver drivers#
- Python record data from globalsat bu 353 s4 usb gps receiver driver#
In order to run a Monkeyrunner script you need to use the monkeyrunner compiler available in the android-sdk/tools directory, rather than python to run your python code.
Python record data from globalsat bu 353 s4 usb gps receiver apk#
Allows you to perform touch events, apk installations, start activities
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performing gestures, pressing buttons, dragging and scrolling
Python record data from globalsat bu 353 s4 usb gps receiver serial#
The Bayesian regression models from this study can be used in active safety systems to model drivers’ comfort in overtaking maneuvers.I’m working on a project using a Globalsat BU353S4 GPS receiver but I’ve had garbled messages being returned when reading the data through the serial connection. Results suggest that pedestrian safety is particularly endangered in situations when the pedestrian is walking opposite to traffic, close to the lane, and when oncoming traffic is present.
Python record data from globalsat bu 353 s4 usb gps receiver driver#
This study is the first to analyze driver behavior when overtaking pedestrians, based on field test and naturalistic driving data. The time-to-collision at the moment of steering away was comparable in value to the time-to-collision used by Euro NCAP for testing active safety systems in car-to-pedestrian longitudinal scenarios since 2018. The regression models predicted distributions similar to those actually observed in the data. Minimum lateral clearance and time-to-collision were only weakly correlated with overtaking speed.
Python record data from globalsat bu 353 s4 usb gps receiver drivers#
Results showed that drivers maintained smaller minimum lateral clearance and lower overtaking speed when the pedestrian was walking in the opposite direction, on the lane edge, or when oncoming traffic was present.
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Bayesian regression was used to model how minimum lateral clearance and overtaking speed depended on the three factors. Seventy-seven overtaking maneuvers in France from the naturalistic driving study UDRIVE and 297 maneuvers in Sweden from field tests were analyzed. The influence of three factors on the safety metrics was investigated: 1) walking direction (same as the overtaking vehicle or opposite), 2) walking position (on the edge of the vehicle lane or 0.5 m away from the edge on the paved shoulder), and 3) oncoming traffic (absent or present). We focused our analysis on how drivers adjust their behavior with respect to three safety metrics (in order of importance): 1) minimum lateral clearance when passing the pedestrian, 2) overtaking speed at that moment, and 3) the time-to-collision at the moment of steering away to start the overtaking maneuver. The aim of this study was, instead, to address pedestrian-overtaking maneuvers on rural roads. Previous modeling of driver behavior in interactions with pedestrians primarily focused on road crossing scenarios.
![python record data from globalsat bu-353-s4 usb gps receiver python record data from globalsat bu-353-s4 usb gps receiver](https://i.pinimg.com/originals/6e/a9/d9/6ea9d976484f78fa13b71971c50c314f.jpg)
To develop active safety systems that avoid such crashes, it is necessary to understand and model driver behavior during the overtaking maneuvers, so that system interventions are acceptable because they happen outside drivers’ comfort zone. In fact, hitting the pedestrian during an overtaking attempt is a common crash scenario. For pedestrians, the risk of dying in a traffic accident is highest on rural roads, which are often characterized by a lack of sidewalks and high traffic speed.