Autonomous Vehicle Lane Change Cost Profiles for Trajectory Selection
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Solution Overview
Problem
Autonomous vehicles face challenges in making complex lane change decisions due to the multifaceted balancing of factors such as lane width, speed limits, weather, visibility, and traffic patterns, which existing systems struggle to efficiently incorporate into their navigation algorithms.
Innovation Solution
A global cost framework for evaluating candidate trajectories that includes semantically meaningful component costs, such as route, traffic, and dynamic costs, to implicitly encode lane change decisions, allowing for continuous lane movement and reducing computational complexity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If discrete decision-making approaches are used for lane changes, then the decision process becomes simpler and more direct, but the system cannot capture the nuanced balancing of multiple factors that influences natural driving behavior
Solution Approach 1:
The patent transforms the lane change decision problem from discrete decisions to continuous parameter optimization. By representing lane changes as continuous trajectory parameters and evaluating them through a cost function that balances multiple factors (safety, efficiency, comfort, lane discipline), the system captures nuanced driving behavior while maintaining computational tractability through parameter-based evaluation rather than combinatorial decision trees
2Device complexity
If explicit discrete lane change decisions are implemented, then the control logic becomes more straightforward, but computational complexity increases due to evaluating multiple candidate trajectories with discrete lane change events
Solution Approach 1:
The patent replaces the mechanical decision-making process (discrete lane change events triggered by specific conditions) with a field-based cost evaluation system. Instead of checking discrete conditions and triggering lane changes, the system continuously evaluates trajectories through a cost function field that implicitly guides the vehicle along optimal paths, eliminating the need for explicit discrete decision logic while reducing computational burden through efficient cost propagation
3Measurement precision
If the system evaluates multiple candidate trajectories with explicit lane change decisions, then navigation accuracy improves, but the computational resources required increase significantly
Solution Approach 1:
The patent applies partial action by evaluating trajectories to a sufficient degree of accuracy rather than exhaustively exploring all possible discrete lane change combinations. The cost function provides a hierarchical evaluation that prunes suboptimal trajectories early in the evaluation process, achieving high navigation accuracy by focusing computational resources on promising candidate trajectories while discarding others based on cost thresholds
Data Source
AI summary
An example method for controlling an autonomous vehicle includes generating, using perception data, a dynamic cost profile associated with an object in the environment; obtaining map data describing lanes of the multilane roadway; generating, using the map data, a lane cost profile that includes a plurality of basins respectively associated with the lanes; generating a candidate trajectory for the autonomous vehicle to traverse in the environment; evaluating the candidate trajectory using an aggregate cost of the candidate trajectory, the aggregate cost computed using the lane cost profile and the dynamic cost profile; determining a selected trajectory for execution by the autonomous vehicle based on the evaluation of the candidate trajectory; controlling the autonomous vehicle according to the selected trajectory.