An IMU-based turn prediction system

Many fatal car accidents are rear-ended collisions. To help alleviate this issue, a lightweight vehicle turn prediction system is developed, which identifies the forthcoming turn event at the early stage so that the neighboring vehicles can be notified in advance to prevent traffic accidents. The system uses smartphone sensors and digital maps. The smartphone sensors (a.k.a Inertial measurement unit) collect position information of the vehicle and predict the future position using particle filters. These positions are used to compute the curvature of the vehicle trace. From digital maps, the curvature of the road that the vehicle is currently traveling is also computed. By examining the difference of these two curvatures as well as the speed of the vehicle, the system determines if the vehicle is making a turn at the early stage. The experimental results show that the proposed system can correctly identify all the turns on the road. In addition, the proposed system does not require extra hardware, which allows it for inexpensive large-scale deployment.

[1]  Alexander Verl,et al.  Vehicle tracking using ultrasonic sensors & joined particle weighting , 2013, 2013 IEEE International Conference on Robotics and Automation.

[2]  C. Cseh,et al.  Architecture of the dedicated short-range communications (DSRC) protocol , 1998, VTC '98. 48th IEEE Vehicular Technology Conference. Pathway to Global Wireless Revolution (Cat. No.98CH36151).

[3]  Baigen Cai,et al.  GNSS/INS-based vehicle lane-change estimation using IMM and lane-level road map , 2013, 16th International IEEE Conference on Intelligent Transportation Systems (ITSC 2013).

[4]  Klaus C. J. Dietmayer,et al.  Situation Assessment of an Autonomous Emergency Brake for Arbitrary Vehicle-to-Vehicle Collision Scenarios , 2009, IEEE Transactions on Intelligent Transportation Systems.

[5]  Lars Reger Securely connected vehicles - what it takes to make self-driving cars a reality , 2016, 2016 21th IEEE European Test Symposium (ETS).

[6]  Vincenzo Suraci,et al.  A Future Internet oriented user centric extended intelligent transportation system , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[7]  Klaus-Dieter Kuhnert,et al.  A lane change detection approach using feature ranking with maximized predictive power , 2014, 2014 IEEE Intelligent Vehicles Symposium Proceedings.

[8]  Hongbin Zha,et al.  Learning lane change trajectories from on-road driving data , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[9]  Mohan M. Trivedi,et al.  Driving style recognition using a smartphone as a sensor platform , 2011, 2011 14th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[10]  Nadeem Akhtar,et al.  Mobile Application for Safe Driving , 2014, 2014 Fourth International Conference on Communication Systems and Network Technologies.

[11]  Rafael Toledo-Moreo,et al.  IMM-Based Lane-Change Prediction in Highways With Low-Cost GPS/INS , 2009, IEEE Transactions on Intelligent Transportation Systems.