Heavy truck modeling and estimation for vehicle-to-vehicle collision avoidance systems

This dissertation details the development of a state and position estimator for articulated heavy trucks based entirely on freely available on-board signals. The estimator consists of a quasi-linear vehicle dynamics model, tire cornering stiffness estimator, Kalman filter, and position integrator. Results from testing show that the estimator can provide lane-level (1.5 m) positioning accuracy in urban environments for the duration of typical GPS outages. A hybrid kinematic-dynamic model allows estimation of hitch angle to within half of a degree over the practical range of articulation angles. This presents novel contributions to the state of the art of trailer tire cornering stiffness estimation and hitch angle estimation.;Government research has estimated that vehicle-to-vehicle (V2V) collision avoidance systems can address 72% of heavy truck crashes, but this requires localization of the truck and trailer in a variety of environments. Studies have shown that GPS cannot be reliably used for V2V in urban and some suburban environments. This estimator offers a potential supplement to GPS for V2V systems in these environments.;Moreover, the current V2V messaging framework does not include an estimate of hitch angle. This can lead to missed warnings and false positives when the implicit assumption of zero hitch angle is grossly violated, such as turning at an intersection. Results from this research indicate that a reliable estimate can be provided without the addition of new sensors.