Abstract
Automated driving technologies offer significant societal benefits but face challenges, particularly in interactions between automated and human-driven vehicles during lane changes and merging on highways. This thesis addresses this issue by focusing on joint driver efforts and proposes a new Communication-Enabled Interaction (CEI) model framework.
Human drivers communicate intent through vehicle kinematics during interactions, making joint decisions and exhibiting joint behaviors. However, current autonomous vehicle (AV) models often lack generalization to real-world behaviors and fail to capture dynamic interactions. AVs typically use models assuming human drivers only respond to AV behavior, leading to over-conservative and sometimes awkward interactions.
To enhance AV-human interactions, the thesis proposes a joint driver model that considers multi-level contributions of drivers. It critiques existing models, highlighting their limitations in capturing dynamic interactions. For instance, many models only consider single drivers and fail to address communication and continuous behavioral adaptation.
The CEI model framework explicitly accounts for driver communication and integrates deterministic future plans with probabilistic beliefs. This framework acknowledges that humans do not continuously optimize behavior but seek satisfactory solutions. The thesis presents a case study where the CEI model accurately describes merging scenarios, generating human-like gap-keeping behavior.
Further, the thesis explores naturalistic driving behaviors using the HighD dataset and develops visualization tools to validate driver models. It extracts and analyzes similar driving scenarios to understand variability in human responses, both operationally and tactically. Controlled experiments in simulators examine driver behaviors during merging conflicts, revealing insights into acceleration control and conflict resolution.
The empirical findings inspire improvements to the CEI model, incorporating intermittent piecewise-constant control observed in human drivers. This updated model accurately reproduces joint driver behaviors and outcomes from experimental scenarios, emphasizing the importance of individual contributions to joint safety margins.
In conclusion, the thesis contributes valuable insights into human lane-changing and merging interactions, proposing a robust model framework for AVs to understand and emulate human driver behaviors. While the study focuses on simplified scenarios, it lays the groundwork for extending the model to more complex real-world situations. The work represents a significant step toward enhancing autonomous vehicles' ability to interact safely and effectively with human drivers on the road.
Human drivers communicate intent through vehicle kinematics during interactions, making joint decisions and exhibiting joint behaviors. However, current autonomous vehicle (AV) models often lack generalization to real-world behaviors and fail to capture dynamic interactions. AVs typically use models assuming human drivers only respond to AV behavior, leading to over-conservative and sometimes awkward interactions.
To enhance AV-human interactions, the thesis proposes a joint driver model that considers multi-level contributions of drivers. It critiques existing models, highlighting their limitations in capturing dynamic interactions. For instance, many models only consider single drivers and fail to address communication and continuous behavioral adaptation.
The CEI model framework explicitly accounts for driver communication and integrates deterministic future plans with probabilistic beliefs. This framework acknowledges that humans do not continuously optimize behavior but seek satisfactory solutions. The thesis presents a case study where the CEI model accurately describes merging scenarios, generating human-like gap-keeping behavior.
Further, the thesis explores naturalistic driving behaviors using the HighD dataset and develops visualization tools to validate driver models. It extracts and analyzes similar driving scenarios to understand variability in human responses, both operationally and tactically. Controlled experiments in simulators examine driver behaviors during merging conflicts, revealing insights into acceleration control and conflict resolution.
The empirical findings inspire improvements to the CEI model, incorporating intermittent piecewise-constant control observed in human drivers. This updated model accurately reproduces joint driver behaviors and outcomes from experimental scenarios, emphasizing the importance of individual contributions to joint safety margins.
In conclusion, the thesis contributes valuable insights into human lane-changing and merging interactions, proposing a robust model framework for AVs to understand and emulate human driver behaviors. While the study focuses on simplified scenarios, it lays the groundwork for extending the model to more complex real-world situations. The work represents a significant step toward enhancing autonomous vehicles' ability to interact safely and effectively with human drivers on the road.
| Original language | English |
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| Qualification | Doctor of Philosophy |
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| Award date | 17 May 2024 |
| Print ISBNs | 978-94-6384-577-9 |
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| Publication status | Published - 2024 |