This study addresses problems in pilots' interaction with conventional traffic advisory systems during simulated visual flight. A predictive traffic visualization concept is proposed, which attempts to fill the gap between the traffic information needed by pilots in visual flight and the information provided by conventional traffic advisory systems. The method and the reasoning for the need to display predictive traffic features are explained. Four display variants that combined various predictive features and a conventional display were evaluated in an experiment with 21 pilots. The pilots' tasks were to watch simulated traffic scenarios on a display and to make conflict and priority decisions. They also provided ratings for mental demand and display preferences. The results show that pilots preferred two display variants: one which combined oriented air vehicle symbols and the relative track, and one with an additional priority cue. With both display variants, the pilots made significantly faster conflict decisions and reported lower mental demand as compared with the variants using conventional TCAS-like symbols and other predictive cues such as the oriented air vehicle symbols and the absolute track. These findings can be applied to improve future traffic advisory systems for visual flight.

In this simulator study, we evaluated the benefits and limitations of a collision warning system for flight according to visual flight rules (VFR). Pilots were confronted with single and multiple traffic, visible both on a moving map display and on the visual system of a simulator. The results demonstrate the benefits of the collision warning system in identifying traffic. However, the avoidance maneuvers initiated by the pilots did not always conform to the regulations. Under multiple traffic conditions, the pilots exhibited slower reaction times when confronted with the traffic warning. They also reported higher levels of workload and reduced situational awareness in comparison to the conditions of single traffic. Using the pilots' feedback, we identified technical requirements for the future development of collision avoidance systems for the VFR.

Nowadays, among the microscopic traffic flow modeling approaches, the car-following models are increasingly used by transportation experts to utilize appropriate intelligent transportation systems. Unlike previous works, where the reaction delay is considered to be fixed, in this paper, a modified neural network approach is proposed to simulate and predict the car-following behavior based on the instantaneous reaction delay of the driver-vehicle unit as the human effects. This reaction delay is calculated based on a proposed idea, and the model is developed based on this feature as an input. In this modeling, the inputs and outputs are chosen with respect to the reaction delay to train the neural network model. Using the field data, the performance of the model is calculated and compared with the responses of some existing neural network car-following models. Considering the difference between the responses of the actual plant and the predicted model as the error, comparison shows that the error in the proposed model is significantly smaller than that that in the other models.

Nowadays, simulation has become a cost-effective option for the evaluation of infrastructure improvements, on-road traffic management systems, and in vehicle driver support systems due to the fast evolution of computational modeling techniques. This paper presents a Locally Linear Neuro-Fuzzy (LLNF) model to simulate and predict the future behavior of a Driver-Vehicle-Unit (DVU). Local Linear Model Tree (LOLIMOT) learning algorithm is applied to train the model using real traffic data. This model was developed based on a new idea for estimating the instantaneous reaction of DVU, as an input of LLNF model. The model?s performance was evaluated based on real observed traffic data and also through comparisons with the results of LLNF models based on constant reaction delay. The results showed that LLNF model based on instantaneous reaction delay input outperformed the other car following models.

We present the geometric consistency filter, an approach to extraction of spatial information in the context of the navigation for autonomous mobile robots. The geometric consistency filter can detect a significant portion of local inconsistencies in geometric world models, which result from partially dynamic environments and a limited resolution of the sensing systems. Belief about consistency of portions of the world model is based on incremental fusion of evidence, which is obtained through analysis of the localization process. Presented experiments demonstrate how the estimation of consistency can be useful for robust localization and updating of world models in dynamic environments.

The presented context-based approach to feature extraction allows accurate, efficient, and reliable world modelling for mobile robots equipped with wide angle sonars. Densely sampled raw sensor data are clustered online in such a way that the impact of the sonar's angular uncertainty is reduced to a great extent, which allows discrimination between linear and punctual objects as well as accurate determination of their positions relative to the robot. The proposed method works also in partially cluttered environments and it is not limited to a specific sensor configuration or motion planning. The resolution and reliability are achieved by considering the physical properties of the sonars as well as the sequences of sensor states and the corresponding range measurements.