@article{ReschkeNeumannBerlitz, author = {Reschke, Johannes and Neumann, Cornelius and Berlitz, Stephan}, title = {Personalised neural networks for a driver intention prediction: communication as enabler for automated driving}, series = {Advanced Optical Technologies}, volume = {9}, journal = {Advanced Optical Technologies}, number = {6}, publisher = {De Gruyter}, doi = {10.1515/aot-2020-0035}, pages = {357 -- 364}, abstract = {In everyday traffic, pedestrians rely on informal communication with other road users. In case of auto� mated vehicles, this communication can be replaced by light signals, which need to be learned beforehand. Prior to an extensive introduction of automated vehicles, a learning phase for these light signals can be set up in manual driving with help of a driver intention prediction. Therefore, a three-staged algorithm consisting of a neural network, a random forest and a conditional stage, is implemented. Using this algorithm, a true-positive rate (TPR) of 94.0\% for a 5.0\% false-positive rate (FPR) can be achieved. To improve this process, a personalization pro� cedure is implemented, using driver-specific behaviours, resulting in TPRs ranging from 91.5 to 96.6\% for a FPR of 5.0\%. Transfer learning of neural networks improves the prediction accuracy of almost all drivers. In order to introduce the implemented algorithm in today's traffic, especially the FPR has to be improved considerably.}, language = {en} } @article{ReschkeAuburgerMarichalaretal., author = {Reschke, Johannes and Auburger, Marie-Theres and Marichalar, Rebeca and Neumann, Cornelius}, title = {Communication between automated vehicles and pedestrians}, series = {ATZ worldwide}, volume = {121}, journal = {ATZ worldwide}, number = {9}, publisher = {Springer Nature}, address = {Wiesbaden}, doi = {10.1007/s38311-019-0098-z}, pages = {16 -- 23}, language = {en} }