Bicyclist Maneuver Type Prediction using Bidirectional Long Short-Term Memory Neural Networks

In the present research, a bicycle simulator is used to study the behavior of bicyclists and to collect data that can be used for developing a Bidirectional Long Short-Term Network (B-LSTM) model that predicts a bicyclist’s intended maneuver type class (left turn, right turn, straight) at an intersection approach. First the bicyclists’ dynamic behavior and the explicit and implicit communication behavior is recorded. Features describing the bicyclists’ behavior are extracted and associated with the respective maneuver type. The B-LSTM model is then trained on the bicycle simulator dataset. Five different model cases are investigated in order to identify the optimal input feature set and evaluate the added value from the inclusion of the bicyclists’ explicit and implicit communication behavioral data in the classification task. A maximum f1-score value of 83.8% (prediction accuracy per maneuver type: 85.3% left-turn, 84% right turn and 82.5% straight) is achieved using classes of implicit and explicit communication behavior together with the dynamic behavior data for the maneuver type prediction. Possible application areas of the proposed model may include, but are not limited to, the expansion and support of existing models and functions in the field of automated driving and the improvement of traffic efficiency and safety through real-time monitoring and forecasting of bicyclist behavior at intersections.     «

In the present research, a bicycle simulator is used to study the behavior of bicyclists and to collect data that can be used for developing a Bidirectional Long Short-Term Network (B-LSTM) model that predicts a bicyclist’s intended maneuver type class (left turn, right turn, straight) at an intersection approach. First the bicyclists’ dynamic behavior and the explicit and implicit communication behavior is recorded. Features describing the bicyclists’ behavior are extracted and associated with...     »