Maximum Likelihood Multiple Model Filtering for Path Prediction in Intelligent Transportation Systems

Abstract Path prediction is an important step in many automotive applications. The idea is to develop a system which allows a vehicle to predict its own path as well as the path of the vehicles in its vicinity. This helps the driver to acquire an enhanced perception of the road environment. In this paper, we develop a novel filtering method to track the movements of an ego vehicle using measurements from GPS sensors. Vehicle maneuver is captured using different kinematic models. In order to combine the strengths of different models, the proposed filter performs a maximum likelihood selection of model-dependent filter estimates. The proposed filter is called the Maximum Likelihood Multiple Model (MLMM) filter. We show that the MLMM filter can provide sub-meter accuracy in terms of position estimation and works well even when a significant fraction of measurements are missing and with diverse trajectories including those having many curved road segments.

[1]  Stewart Worrall,et al.  The Warrigal Dataset: Multi-Vehicle Trajectories and V2V Communications , 2014, IEEE Intelligent Transportation Systems Magazine.

[2]  Dizan Vasquez,et al.  A survey on motion prediction and risk assessment for intelligent vehicles , 2014, ROBOMECH Journal.

[3]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.

[4]  Marcus Obst,et al.  Empirical evaluation of vehicular models for ego motion estimation , 2011, 2011 IEEE Intelligent Vehicles Symposium (IV).

[5]  Jianqiang Wang,et al.  Vehicle Trajectory Prediction by Integrating Physics- and Maneuver-Based Approaches Using Interactive Multiple Models , 2018, IEEE Transactions on Industrial Electronics.

[6]  Dryver Huston,et al.  Intelligent Forecasting Using Dead Reckoning With Dynamic Errors , 2016, IEEE Transactions on Industrial Informatics.

[7]  Dryver Huston,et al.  Trajectory Estimations Using Smartphones , 2015, IEEE Transactions on Industrial Electronics.

[8]  V. Jilkov,et al.  Survey of maneuvering target tracking. Part V. Multiple-model methods , 2005, IEEE Transactions on Aerospace and Electronic Systems.

[9]  Qi Cheng,et al.  GNSS/Electronic Compass/Road Segment Information Fusion for Vehicle-to-Vehicle Collision Avoidance Application , 2017, Sensors.

[10]  Fabian de Ponte Muller.  Survey on Ranging Sensors and Cooperative Techniques for Relative Positioning of Vehicles. , 2017 .

[11]  Christoph Stiller,et al.  Driver intent inference at urban intersections using the intelligent driver model , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[12]  X. R. Li,et al.  Survey of maneuvering target tracking. Part I. Dynamic models , 2003 .

[13]  C. S. George Lee,et al.  Design of an interactive multiple model based two-stage multi-vehicle tracking algorithm for autonomous navigation , 2015, 2015 IEEE Intelligent Vehicles Symposium (IV).

[14]  Angelos Amditis,et al.  An Advanced Cooperative Path Prediction Algorithm for Safety Applications in Vehicular Networks , 2011, IEEE Transactions on Intelligent Transportation Systems.

[15]  Gerd Wanielik,et al.  High-accurate vehicle localization using digital maps and coherency images , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[16]  C. L. Philip Chen,et al.  Data-intensive applications, challenges, techniques and technologies: A survey on Big Data , 2014, Inf. Sci..