Towards Incorporating Contextual Knowledge into the Prediction of Driving Behavior
This work addresses the need for more accurate driving behavior prediction in advanced driver assistance systems and autonomous driving, though it is incremental as it builds on existing methods by adding contextual factors.
The study tackled the problem of predicting driving behavior by incorporating contextual knowledge, specifically traffic density, into motion prediction algorithms, resulting in a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of 0.18m over a 5-second horizon.
Predicting the behavior of surrounding traffic participants is crucial for advanced driver assistance systems and autonomous driving. Most researchers however do not consider contextual knowledge when predicting vehicle motion. Extending former studies, we investigate how predictions are affected by external conditions. To do so, we categorize different kinds of contextual information and provide a carefully chosen definition as well as examples for external conditions. More precisely, we investigate how a state-of-the-art approach for lateral motion prediction is influenced by one selected external condition, namely the traffic density. Our investigations demonstrate that this kind of information is highly relevant in order to improve the performance of prediction algorithms. Therefore, this study constitutes the first step towards the integration of such information into automated vehicles. Moreover, our motion prediction approach is evaluated based on the public highD data set showing a maneuver prediction performance with areas under the ROC curve above 97% and a median lateral prediction error of only 0.18m on a prediction horizon of 5s.