CCTR: Calibrating Trajectory Prediction for Uncertainty-Aware Motion Planning in Autonomous Driving

Autonomous driving systems rely on precise trajectory prediction for safe and efficient motion planning. Despite considerable efforts to enhance prediction accuracy, inherent uncertainties persist due to data noise and incomplete observations. Many strategies entail formalizing prediction outcomes into distributions and utilizing variance to...

基于Lambert-Beer理论与人工神经网络的混合荧光粉发射光谱预测

The emission spectra of high color rendering phosphors, mixed with the yttrium aluminium garnet, silicon based oxynitride and nitride based phosphors, were predicted by the Lambert-Beer theory and back propagation neural network (BP NN). Firstly, the modified Lambert-Beer model was used to calculate the proportional coefficient of the...