A Learning-Based Framework for Two-Dimensional Vehicle Maneuver Prediction over V2V Networks

Situational awareness in vehicular networks could be substantially improved utilizing reliable trajectory prediction methods. More precise situational awareness, in turn, results in notably better performance of critical safety applications, such as Forward Collision Warning (FCW), as well as comfort applications like Cooperative Adaptive Cruise Control (CACC). Therefore, vehicle trajectory prediction problem needs to be deeply investigated in order to come up with an end to end framework with enough precision required by the safety applications' controllers. This problem has been tackled in the literature using different methods. However, machine learning, which is a promising and emerging field with remarkable potential for time series prediction, has not been explored enough for this purpose. In this paper, a two-layer neural network-based system is developed which predicts the future values of vehicle parameters, such as velocity, acceleration, and yaw rate, in the first layer and then predicts the two-dimensional, i.e. longitudinal and lateral, trajectory points based on the first layer's outputs. The performance of the proposed framework has been evaluated in realistic cut-in scenarios from Safety Pilot Model Deployment (SPMD) dataset and the results show a noticeable improvement in the prediction accuracy in comparison with the kinematics model which is the dominant employed model by the automotive industry. Both ideal and nonideal communication circumstances have been investigated for our system evaluation. For non-ideal case, an estimation step is included in the framework before the parameter prediction block to handle the drawbacks of packet drops or sensor failures and reconstruct the time series of vehicle parameters at a desirable frequency

Design, development and test of shuttle/Centaur G-prime cryogenic tankage thermal protection systems

The thermal protection systems for the shuttle/Centaur would have had to provide fail-safe thermal protection during prelaunch, launch ascent, and on-orbit operations as well as during potential abort. The thermal protection systems selected used a helium-purged polyimide foam beneath three rediation shields for the liquid-hydrogen tank and radiation shields only for the liquid-oxygen tank (three shields on the tank sidewall and four on the aft bulkhead). A double-walled vacuum bulkhead separated the two tanks. The liquid-hydrogen tank had one 0.75-in-thick layer of foam on the forward bulkhead and two layers on the larger area sidewall. Full scale tests of the flight vehicle in a simulated shuttle cargo bay that was purged with gaseous nitrogen gave total prelaunch heating rates of 88,500 Btu/hr and 44,000 Btu/hr for the liquid-hydrogen and -oxygen tanks, respectively. Calorimeter tests on a representative sample of the liquid-hydrogen tank sidewall thermal protection system indicated that the measured unit heating rate would rapidly decrease from the prelaunch rate of approx 100 Btu/hr/sq ft to a desired rate of less than 1.3 Btu/hr/sq ft once on orbit