Description
Taylor and Francis Predicting Vehicle Trajectory 2017 Edition by Cesar Barrios, Yuichi Motai
This book concentrates on improving the prediction of a vehicle's future trajectory, particularly on non-straight paths. Having an accurate prediction of where a vehicle is heading is crucial for the system to reliably determine possible path intersections of more than one vehicle at the same time. The US DOT will be mandating that all vehicle manufacturers begin implementing V2V and V2I systems, so very soon collision avoidance systems will no longer rely on line of sight sensors, but instead will be able to take into account another vehicle's spatial movements to determine if the future trajectories of the vehicles will intersect at the same time. Furthermore, the book introduces the reader to some improvements when predicting the future trajectory of a vehicle and presents a novel temporary solution on how to speed up the implementation of such V2V collision avoidance systems. Additionally, it evaluates whether smartphones can be used for trajectory predictions, in an attempt to populate a V2V collision avoidance system faster than a vehicle manufacturer can. TABLE OF CONTENTSPREFACECHAPTER 1: Improving Estimation of Vehicle's Trajectory Using Latest Global Positioning System withKalman Filtering1.1. Introduction1.2. Kalman Filter1.3. Interacting Multiple Models Estimation1.4. Geographical Information System1.5. Experimental Results1.6. Conclusions1.7. ReferencesCHAPTER 2: Asynchronous Heterogeneous Sensor Fusion using Dead Reckoning and Kalman Filters2.1. Introduction2.2. Position Estimation Techniques2.3. Dead Reckoning with Dynamic Error (DRWDE) using Kalman Filters2.4. Evaluation Criteria2.5. Experimental Performance of the DRWDE System2.6. Conclusions2.7. ReferencesCHAPTER 3: Can Smartphones Fill in the V2V/V2I Implementation Gap?3.1. Introduction3.2. Position Estimation with Kalman Filters3.3. Position Estimation Framework Using GPS and Accelerometer Sensors3.4. Car and Smartphone Sensors Setup for a V2V/V2I System3.5. Evaluation Criteria3.6. Experimental Evaluation3.7. Conclusions3.8. ReferencesCHAPTER 4: ConclusionsAppendix:A.1 Acronym DefinitionsA.2 Symbol DefinitionsA.3 Mathematical limitation for improved estimationsA.4 Taylor polynomial representation with its respective errorA.5 Proof of the expected value calculationsA.6 Representative Visual Basic codeA.7 Representative Matlab code