Sub-full Model-Based Heterogeneous Sensor Fusion for Lateral State Estimation of Preceding Target Vehicles

Trajectory planning of an automated vehicle requires behavior knowledge of preceding target vehicles (PTVs), and lateral states, such as lateral velocity and yaw rate are key enablers for precise behavior description. However, lateral states of a PTV can hardly be measured directly by common onboard sensors. In addition, although these states can be transmitted via vehicle to vehicle (V2V), the accuracy is limited by the state holding process during the communication interval. Aiming at improved PTV lateral states, this article considers the estimation of these states using visual and V2V measurements, and proposes a three-stage fusion architecture consisting of input estimator, heterogeneous model-based local estimators, and fusion center. Specifically, to cope with the low rate of the received steering angle, the input estimator is constructed to obtain high rate control inputs. In the local estimator design, unlike the conventional modeling problems mixing control input errors with model errors in process noise, this paper separates them and develops a model adaption algorithm to compensate the time-varying covariance of the errors of the estimated control inputs. Then, to fuse the heterogeneous local estimates in the fusion center, a subfull model-based information matrix filter is designed to address this specific heterogeneous fusion problem, in which the local models are treated as submodels of a common full model. Hardware-in-the-loop experimental results show that the proposed method gives more accurate estimates in comparison with other approaches.

[1]  Henry Leung,et al.  Overview of Environment Perception for Intelligent Vehicles , 2017, IEEE Transactions on Intelligent Transportation Systems.

[2]  Samyeul Noh,et al.  Decision-Making Framework for Automated Driving in Highway Environments , 2018, IEEE Transactions on Intelligent Transportation Systems.

[3]  Tao Chen,et al.  Intelligent Hybrid Electric Vehicle ACC With Coordinated Control of Tracking Ability, Fuel Economy, and Ride Comfort , 2015, IEEE Transactions on Intelligent Transportation Systems.

[4]  Jing Ma,et al.  Multi-sensor distributed fusion estimation with applications in networked systems: A review paper , 2017, Inf. Fusion.

[5]  Yuchuan Du,et al.  Velocity Control Strategies to Improve Automated Vehicle Driving Comfort , 2018, IEEE Intelligent Transportation Systems Magazine.

[6]  Rajesh Rajamani,et al.  Tracking of Vehicle Motion on Highways and Urban Roads Using a Nonlinear Observer , 2019, IEEE/ASME Transactions on Mechatronics.

[7]  Thiagalingam Kirubarajan,et al.  Estimation with Applications to Tracking and Navigation , 2001 .

[8]  Ruzena Bajcsy,et al.  Integrating Intuitive Driver Models in Autonomous Planning for Interactive Maneuvers , 2017, IEEE Transactions on Intelligent Transportation Systems.

[9]  James Lam,et al.  A Novel Observer Design for Simultaneous Estimation of Vehicle Steering Angle and Sideslip Angle , 2016, IEEE Transactions on Industrial Electronics.

[10]  Chengliang Yin,et al.  Host–Target Vehicle Model-Based Lateral State Estimation for Preceding Target Vehicles Considering Measurement Delay , 2018, IEEE Transactions on Industrial Informatics.

[11]  Qi Wang,et al.  Tracking as a Whole: Multi-Target Tracking by Modeling Group Behavior With Sequential Detection , 2017, IEEE Transactions on Intelligent Transportation Systems.

[12]  Chung Choo Chung,et al.  Vehicle Path Prediction Using Yaw Acceleration for Adaptive Cruise Control , 2018, IEEE Transactions on Intelligent Transportation Systems.

[13]  Junmin Wang,et al.  Driver-Assistance Lateral Motion Control for In-Wheel-Motor-Driven Electric Ground Vehicles Subject to Small Torque Variation , 2018, IEEE Transactions on Vehicular Technology.

[14]  Hao Zhang,et al.  Collision-Free Navigation of Autonomous Vehicles Using Convex Quadratic Programming-Based Model Predictive Control , 2018, IEEE/ASME Transactions on Mechatronics.

[15]  Torsten Bertram,et al.  Track-to-Track Fusion With Asynchronous Sensors Using Information Matrix Fusion for Surround Environment Perception , 2012, IEEE Transactions on Intelligent Transportation Systems.

[16]  Xin Tian,et al.  Heterogeneous track-to-track fusion , 2011, 14th International Conference on Information Fusion.

[17]  Yunmin Zhu,et al.  The optimality for the distributed Kalman filtering fusion with feedback , 2001, Autom..

[18]  Mark A. Minor,et al.  Curvature-Based Ground Vehicle Control of Trailer Path Following Considering Sideslip and Limited Steering Actuation , 2017, IEEE Transactions on Intelligent Transportation Systems.

[19]  Rolf Isermann,et al.  Cornering Stiffness and Sideslip Angle Estimation for Integrated Vehicle Dynamics Control , 2016 .

[20]  Uwe D. Hanebeck,et al.  Decentralized data fusion with inverse covariance intersection , 2017, Autom..

[21]  Senem Velipasalar,et al.  Robust and Computationally Lightweight Autonomous Tracking of Vehicle Taillights and Signal Detection by Embedded Smart Cameras , 2015, IEEE Transactions on Industrial Electronics.

[22]  Stephan Sand,et al.  Analysis of Communication Requirements for CACC in Stop-and-Go Behavior for Energy Efficient Driving , 2016, 2016 IEEE 84th Vehicular Technology Conference (VTC-Fall).

[23]  Jean-Charles Noyer,et al.  A Model-Based Joint Detection and Tracking Approach for Multi-Vehicle Tracking With Lidar Sensor , 2015, IEEE Transactions on Intelligent Transportation Systems.

[24]  John B. Kenney,et al.  Dedicated Short-Range Communications (DSRC) Standards in the United States , 2011, Proceedings of the IEEE.

[25]  Jian Wang,et al.  Cooperative Localization of Connected Vehicles: Integrating GNSS With DSRC Using a Robust Cubature Kalman Filter , 2017, IEEE Transactions on Intelligent Transportation Systems.

[26]  Jonas Fredriksson,et al.  Lane Change Maneuvers for Automated Vehicles , 2017, IEEE Transactions on Intelligent Transportation Systems.

[27]  Yanjun Huang,et al.  Path Planning and Tracking for Vehicle Collision Avoidance Based on Model Predictive Control With Multiconstraints , 2017, IEEE Transactions on Vehicular Technology.

[28]  Mohan M. Trivedi,et al.  How Would Surround Vehicles Move? A Unified Framework for Maneuver Classification and Motion Prediction , 2018, IEEE Transactions on Intelligent Vehicles.

[29]  Francesco Borrelli,et al.  A Novel Approach for Vehicle Inertial Parameter Identification Using a Dual Kalman Filter , 2015, IEEE Transactions on Intelligent Transportation Systems.

[30]  Li Zhao,et al.  Vehicle-to-Everything (v2x) Services Supported by LTE-Based Systems and 5G , 2017, IEEE Communications Standards Magazine.

[31]  Martin Lauer,et al.  3D Traffic Scene Understanding From Movable Platforms , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[32]  Wim Desmet,et al.  Design and Experimental Validation of a Stable Two-Stage Estimator for Automotive Sideslip Angle and Tire Parameters , 2017, IEEE Transactions on Vehicular Technology.

[33]  Yoichi Hori,et al.  Vision-Based Lateral State Estimation for Integrated Control of Automated Vehicles Considering Multirate and Unevenly Delayed Measurements , 2018, IEEE/ASME Transactions on Mechatronics.

[34]  Joseph L. Mundy,et al.  Vehicle Surveillance with a Generic, Adaptive, 3D Vehicle Model , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[35]  Yoichi Hori,et al.  Multirate Estimation and Control of Body Slip Angle for Electric Vehicles Based on Onboard Vision System , 2014, IEEE Transactions on Industrial Electronics.