Abstract Driver behavior impacts traffic safety, fuel/energy consumption and gas emissions. Driver behavior profiling tries to understand and positively impact driver behavior. Usually driver behavior profiling tasks involve automated collection of driving data and application of computer models to generate a classification that characterizes the driver aggressiveness profile. Different sensors and classification methods have been employed in this task, however, low-cost solutions and high performance are still research targets. This paper presents an investigation with different Android smartphone sensors, and classification algorithms in order to assess which sensor/method assembly enables classification with higher performance. The results show that specific combinations of sensors and intelligent methods allow classification performance improvement.
1 Introduction Driver behavior strongly impacts traffic security [] and causes the vast majority of motor vehicle accidents []. In 2010, the total economic cost of motor vehicle crashes in the United States was 242 billion []. This figure represents the costs for approximately 33 thousand fatalities, 4 million nonfatal injuries, and 24 million damaged vehicles. Driver behavior adaptations might increase overall security and lessen vehicle fuel/energy consumption and gas emissions [, ]. In this context, driver behavior profiling tries to better understand and potentially improve driver behavior, leveraging a safer and more energy aware driving. Driver monitoring and analysis or driver behavior profiling is the process of automatically collecting driving data (e.g., speed, acceleration, breaking, steering, location) and applying a computational model to them in order to generate a safety score for the driver. Driving data collection may be achieved by several kinds of sensors, from the general ones in smartphones, to dedicated equipment such as monitoring cameras, telematics boxes, and On-Board Diagnostic (OBD) adapters.
Modern smartphones provide sensors suitable to collect data for driver profile analysis. Previous work [–] shows that properly preprocessed and handled smartphone sensors data are an interesting alternative to conventional black boxes for the monitoring of driver behavior. Driver behavior profiling relevance has grown in the last few years. In the insurance telematics domain, plans such as Usage-Based Insurance (UBI) or Pay-How-You-Drive (PHYD) make car insurance cheaper by rewarding drivers with good driving scores, instead of only considering group based statistics (e.g., age, gender, marital status) for that end. In the freight management domain, automated, continuous, and real-time driver behavior profiling enables managers to institutionalize campaigns aiming to improve drivers score, and, as a consequence, decrease accidents, and increase resource economy, and vehicle lifetime.
Nov 23, 2005. Sensors Series. Senior Series Editor: B E Jones. Series Co-Editor: W B Spillman, Jr. Novel Sensors and Sensing. Hall Effect Devices, Second. 1993 Technology of Electrical Measurements, John Wiley & Sons. Automotive Sensors 2002) (permission of Robert Bosch GmbH). Original research article: Pages 0. Yanxiang Zhang, Kongfa Chen, Changrong Xia, San Ping Jiang, Meng Ni. Download PDF. Article preview. Heli Wang, Todd Deutsch, Adam Welch, John A. Off-board regeneration of ammonia borane for use as a hydrogen carrier for automotive fuel cells.
Furthermore, notifications of unsafe driving events presented to drivers in real-time can help prevent accidents. For example, a smartphone app may notify the driver every time she performs an aggressive turn. Yugioh Pc Download Utorrent.
Several driver behavior profiling work [–] use a smartphone based sensor-fusion to identify aggressive driving events (e.g. Autodesk 3ds Max Design 2015 Trial Download. , aggressive acceleration, aggressive break) as the basis to calculate driver score. Another work [] uses vehicle sensor data to provide driving tips and assess fuel consumption as a function of driver profile.
The machine learning algorithms (MLAs) employed in these papers come down to fuzzy logic or variations of Dynamic Time Warping (DTW). Dynamic Time Warping is an algorithm to find similar patterns in temporal series. It was originally employed in the speech recognition problem []. Cuisinart Troubleshooting Blender. We believe that other MLAs and sensor combination can be applied to the task of identifying aggressive driving events with promising results.