The kinematic bicycle model: A consistent model for planning feasible trajectories for autonomous vehicles?

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Survey of Deep Reinforcement Learning for Motion Planning of Autonomous Vehicles (2020, methodology): “Though the kinematic model seems quite simpliﬁed, and as stated in [25], such a model can behave signiﬁcantly different from an actual vehicle, though for the many control situations, the accuracy is suitable [24].”
ST-P3: End-to-end Vision-based Autonomous Driving via Spatial-Temporal Feature Learning (2022, methodology): “Formally, given the SDV’s dynamic state, we adopt the bicycle model [40] to sample a set of trajectories τ .”
INTERACTION Dataset: An INTERnational, Adversarial and Cooperative moTION Dataset in Interactive Driving Scenarios with Semantic Maps (2019, methodology): “For vehicles, the RTS smoother uses a bicycle model [33] as process model, allowing to capture the kinematics constraints of vehicles.”
End-to-End Autonomous Driving: Challenges and Frontiers (2023, methodology): “Most existing publicly available simulators use highly simpliﬁed vehicle models, such as the unicycle model [120] or the bicycle model [121].”
MP3: A Unified Model to Map, Perceive, Predict and Plan (2021, background): “, T , to rollout a bicycle model [38] starting from the initial SDV state.”