Autonomous Merge Strategy Using Intention Prediction
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Solution Overview
Problem
Human-driven vehicles often face challenges in cooperating with autonomous vehicles during merging maneuvers, especially in heavy traffic scenarios, as existing autonomous driving systems struggle to understand and emulate human intentions, leading to potential collisions and inefficiencies.
Innovation Solution
An autonomous and cooperative merging system that uses an intention estimator and cost function-based evaluations to predict the intentions of surrounding vehicles, generating candidate driving strategies and selecting the best merge strategy for the host vehicle to facilitate smooth and safe merging.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If autonomous driving systems use traditional reactive control methods for merging maneuvers, then the system responds to immediate traffic conditions, but it fails to understand and predict human driver intentions leading to collisions and inefficiencies
Solution Approach 1:
The system performs preliminary analysis of surrounding vehicles' intentions before executing merging maneuvers. The intention prediction module estimates whether other drivers plan to accelerate or decelerate in advance, allowing the autonomous vehicle to plan merging actions proactively rather than reactively, thereby improving safety by anticipating human driver behavior.
Solution Approach 2:
The system continuously monitors and updates predictions of surrounding vehicles' intentions based on real-time traffic conditions and vehicle behavior patterns. This feedback mechanism allows the autonomous driving system to adapt its merging strategy dynamically, improving reliability by incorporating ongoing information about human driver intentions into decision-making.
2Ease of operation
If drivers perform the same acceleration or deceleration action simultaneously during merging, then individual vehicle control is maintained, but chain braking events occur causing traffic slowdowns or collisions
Solution Approach 1:
The system introduces an intermediary intention prediction layer between individual vehicle control and traffic flow interaction. By predicting and understanding the intentions of surrounding vehicles, the system can mediate merging decisions to avoid simultaneous acceleration or deceleration actions that would otherwise cause chain braking events and traffic disruptions.
Solution Approach 2:
The system performs preliminary assessment of surrounding vehicles' intended actions before executing merging maneuvers. By anticipating whether other drivers plan to accelerate or decelerate, the autonomous vehicle can adjust its own actions in advance to prevent simultaneous opposing actions that would trigger chain braking events and protect against traffic slowdowns or collisions.
Data Source
AI summary
An autonomous driving merge management system includes an autonomous driving control device and an intention decision management system. The management system includes a candidate strategy subsystem generating a plurality of candidate driving strategies, a merging vehicle behavior recognition subsystem predicting a merging intention of a merging vehicle; an intention-based interactive prediction subsystem predicting future merging scenarios between the host vehicle and merging vehicle as a function of inputs by the merging vehicle behavior recognition subsystem and inputs by the candidate strategy subsystem, and a cost function-based evaluation subsystem determining a cost for each future merging scenario generated by the intention-based interactive prediction subsystem. A processor selects a merge strategy of the host vehicle based on intention-based prediction results and cost function-based evaluation results. The autonomous driving control device applies the merge strategy to the host vehicle for allowing the merging vehicle to cooperatively merge with the host vehicle.