Sensing technology for automatic land vehicle.

Remote sensing technology for mapping and monitoring land-cover and land-use change

Automatic vehicle trajectory extraction by aerial remote sensing

Research in road users’ behaviour typically depends on detailed observational data availability, particularly if the interest is in driving behaviour modelling. Among this type of data, vehicle trajectories are an important source of information for traffic flow theory, driving behaviour modelling, innovation in traffic management and safety and environmental studies. Recent developments in sen...

Automatic Human Daily Activity Segmentation Applying Smart Sensing Technology

Human daily activity segmentation utilizing smartphone sensing technology is quite new challenge. In this paper, the segmentation method combining statistical model and time series analysis is designed and implemented. According to designed partition procedure, real measured accelerometer datasets of human daily activities are tested. The segmentation performance of sliding window autocorrelati...

ELVIS: Eigenvectors for Land Vehicle Image System

ELVIS (Eigenvectors for Land Vehicle Image System) is a road-following system designed to drive the CMU Navlabs. It is based on ALVINN, the neural network road-following system built by Dean Pomerleau at CMU. ALVINN provided the motivation for creating ELVIS: although ALVINN is successful, it is not entirely clear why the system works. ELVIS is an attempt to more fully understand ALVINN and to ...

Contingency Planning for an Autonomous Land Vehicle

A route planner for an autonomous land vehicle has been developed that deals explicitly with the incompleteness and uncertainties that can be expected in map data. In particular, the planner finds a preferred route taking into account the potential cost of the detours that may be needed if one or more choke regions are found to be blocked. In addition, it precomputes and saves the alternative o...