Prediction of Cyclists’ Interaction-Aware Trajectory for Cooperative Automated Vehicles

Cooperative behaviour is one of the most crucial factors for safety and comfort in shared traffic spaces. While a human driver might be able to automatically identify behavioural indicators of other traffic participants to predict their movement, an automated vehicle is not. This is especially important in interaction situations with vulnerable road users (VRU), such as cyclists. The focus of this work is to implement, evaluate and compare different possible methods of trajectory forecasts for cyclists in order to estimate their behavioural intention. With accurate trajectory information of the VRU, an automated vehicle might be able to plan a cooperative reaction ahead in time and guarantee a comfortable traffic flow. In sum, three different neural network architectures have been tested with the main focus on a CNN, which is capable of incorporating map data into the trajectory forecast. The results showed, that including external influencing factors, like the infrastructure of a traffic scene, can have a beneficial effect on the accuracy of the cyclist’s predicted movement.