Until fully automated vehicles reach full market saturation, a mixed operation between human-driven and highly automated vehicles will characterize traffic activities. Mutual understanding of driving intentions is therefore crucial for harmonizing road traffic.
The prediction of driving intentions of other road users is a subject of numerous scientific researches and is the link between environmental perception and maneuver planning. The driving environment is determined by kinematic vehicle parameters and their temporal history and as well as the context of the traffic situation. Based on these, predictions about future trajectories of the surrounding vehicles are made possible and one’s own target behavior can be derived in the form of a target trajectory.
However, the influence of one’s own driving behaviour on other road users is only part of very few investigations yet. For the acceptance of highly automated driving functions, it is not only essential that the driving behaviour is perceived safe and comfortable by passengers of a highly automated vehicle, but also predictable by other road users.
The thesis aims to gain specific insights on interactions between road users. The core target of this thesis is to train a model, which describes how a lane change influences the behaviour of rear-end road users on highways. The highD dataset will be used to build a ‘Deep Learning’ model, which learns the dependencies between lane changes and the reactions caused by it.
The goals of this thesis are:
- Defining the scenario
- Identifying the relevant input and output parameters for the deep learning module
- Creating and training of a suitable model
- Using the developed model to provide a reference for mutual interactions between road users and to derive possible behavioural patterns
- Assessing the impact on rear road users when changing lane for highly automated driving