WisDriver:A WiFi and Smartphone Sensing System for Safely Driving There has been an increasing interest to monitor driverxe2x80x99s behavior, vehicles and driving data aim to identify risky driving maneuvers and prevent accidents. Current monitor methods, however, most data obtained from the vehicle sensors are sent to the control units through buses such as: CAN, MOST, LIN and FlexRay, but cannot recognize the dangerous actions of the drivers accurately. Past proposals for addressing this limitation rely on leveraging more sensors attached to drivers and hence significantly increase the cost and affect the driverxe2x80x99s comfort. This paper introduces the first finegrained and devicefree driverxe2x80x99s behavior monitoring system that is accurate to 15 dangerous driving actions. Unlike past work, which only depends on sensors, our system exploits fusion data combining smartphone and WiFi signals to accurately identify risky driving operations. We present the design and implementation of WisDriver and discuss the evaluation by experiment from the 20drivers field trial. Our evaluation results show that WisDriver can achieve high accuracy (92%) in detecting 15 different dangerous driving behaviors. It will have a great significance for assisting the driver and improving traffic safety.

Analyzing Driving Data using the ADAPT Distributed Analytics Platform for Connected Vehicles Connected and autonomous vehicles are changing the future of mobility by providing vehicles connectivity and intelligence for smarter performance. Driver behavior analysis plays an important role in most of these applications. This paper provides a case study using A Distributed Analytics Platform for Telematics (ADAPT) that powers many popular connected car products in the market today. It provides an overview of the platform and some of its supported products. The paper focuses on driver safety data analysis using several novel techniques. It explores image analysis for advanced driver safety using a novel randomized contractive mappingsbased technique for efficient onboard driver data analysis. In addition, it explains how social collective incentive mechanisms can be integrated with the driving scoring functions for improving driver safety. The paper discusses market adoption and presents experimental results.