Extraction and Investigation of Lane Marker Parameters for Steering Signal Prediction

In the work several lane marker parameters, extracted from the visual data that can be used for steering prediction in the intelligent self-learning driver's assistance systems, are presented. Stable parameters that can be used for steering prediction were estimated from the lane marker. The presented parameters show high correlation with a car steering angle. Lane marker detection and lane marker extraction methods from mono-camera images are also considered in the paper

Vehicle Acceleration Prediction Using Specific Road Curvature Points

In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters - speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters

Vehicle Acceleration Prediction Using Specific Road Curvature Points

In the work vehicle acceleration prediction issue is discussed. Three types of parameters are used for prediction system input: CAN-bus parameters - speed and curvature, derived speed parameters and newly offered specific curve point parameters, denoting changes in a curve. The real road data was used for predictions. Road curvature segments were divided into single and S-type curves. Acceleration was predicted using artificial neural networks and look-up table. The look-up table method showed the best results with newly offered specific curve parameters

Vehicle’s Steering Signal Predictions Using Neural Networks

Back-propagation trained neural networks, as well as extreme learning machine (ELM) were used to predict car driverpsilas steering behavior, based on road curvature, velocity and acceleration of a car. Predictions were performed using real-road data, obtained on a test car in a country-road scenario. We made a simplification using gyroscopically measured curvature of the road instead of visually extracted curvature measures. It was found that an optimum exists how far one has to look onto a curvature signal, according to neural network prediction accuracy. Velocity and acceleration did not improve steering signal prediction accuracy in our framework. Traditional neural networks and ELM performed similarly in terms of prediction errors

Extraction and Investigation of Lane Marker Parameters for Steering Signal Prediction

In the work several lane marker parameters, extracted from the visual data that can be used for steering prediction in the intelligent self-learning driver's assistance systems, are presented. Stable parameters that can be used for steering prediction were estimated from the lane marker. The presented parameters show high correlation with a car steering angle. Lane marker detection and lane marker extraction methods from mono-camera images are also considered in the paper