Lightweight Driver Behavior Identification Model with Sparse Learning on In-Vehicle CAN-BUS Sensor Data
نویسندگان
چکیده
منابع مشابه
Analysis and Classification of Driver Behavior using In-Vehicle CAN-Bus Information
This paper describes recent advances in the analysis and classification of driver behavior in actual driving scenarios. We employ data obtained from the UTDrive corpus to model driving behavior and to detect if distraction due to secondary tasks is present. Hidden Markov Models (HMMs) are used to capture the sequence of driving characteristics acquired from the vehicle’s CAN-Bus (Controller Are...
متن کاملThe effects of vehicle model and driver behavior on risk.
We study the dependence of risk on vehicle type and especially on vehicle model. Here, risk is measured by the number of driver fatalities per year per million vehicles registered. We analyze both the risk to the drivers of each vehicle model and the risk the vehicle model imposes on drivers of other vehicles with which it crashes. The "combined risk" associated with each vehicle model is simpl...
متن کاملUAV Data Mule Vehicle Routing Problems In Sparse Sensor Networks
UAV Data Mule Vehicle Routing Problems In Sparse Sensor Networks by Jason Tony Isaacs Recent advances in technology have enabled the use of wireless sensor networks for environmental monitoring and surveillance. Wireless sensor networks are particularly beneficial for monitoring environments that are unsuitable for human presence, such as those arising in the monitoring of permafrost, volcanos,...
متن کاملIntelligent Sensor Based Road Vehicle Driver Assistance
This paper concerns research towards the provision of a rich set of road vehicle driver assistance modes to reduce the stress and improve the safety of negotiating both on-road and offroad terrain. These modes include navigation planning, potential collision warnings, blind spot and rear traffic monitoring, backing assistance, drowsiness detection and night vision enhancement. The approach take...
متن کاملElectric Vehicle Driver Clustering using Statistical Model and Machine Learning
Electric Vehicle (EV) is playing a significant role in the distribution energy management systems since the power consumption level of the EVs is much higher than the other regular home appliances. The randomness of the EV driver behaviors make the optimal charging or discharging scheduling even more difficult due to the uncertain charging session parameters. To minimize the impact of behaviora...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Sensors
سال: 2020
ISSN: 1424-8220
DOI: 10.3390/s20185030