Lane Change Navigation Costing Before Critical Intersections
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Solution Overview
Problem
Conventional machine learning techniques for autonomous vehicle control are ineffective in complicated traffic environments, particularly when determining vehicle control actions for lane changes before critical intersections, as they do not consider a cost function based on the current traffic state.
Innovation Solution
A vehicle behavior control system that assesses a navigation cost for tactical driving decisions using a cost function, incorporating data from perception systems and vehicle-to-vehicle communication to minimize the cost of lane changes before critical intersections, thereby optimizing vehicle control actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional machine learning techniques are used for vehicle control, then the system is simple to implement, but the control effectiveness deteriorates in complicated traffic environments involving complex interactions before critical intersections
Solution Approach 1:
The control system is segmented into multiple specialized modules: a perception module for environmental sensing, a cost function module for evaluating tactical decisions, and a vehicle control module for executing maneuvers. This segmentation allows each module to specialize in specific tasks, improving overall control effectiveness in complex traffic environments while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
A cost function module is introduced as an intermediary between the perception module and the vehicle control module. This intermediary evaluates various tactical driving decisions (lane changes, merges, intersections) by computing navigation costs based on current traffic states, enabling the system to make informed control decisions without directly complexifying the control architecture.
2Measurement precision
If a cost function based on current traffic state is incorporated, then the navigation cost computation improves, but the computational complexity increases
Solution Approach 1:
The system performs preliminary computation of navigation costs for multiple potential tactical decisions before executing any maneuver. By pre-evaluating lane changes, merges, and intersection approaches using the cost function module with current traffic state data, the system prepares optimized control actions in advance, improving decision accuracy while distributing computational load over time rather than requiring intensive real-time computation during critical moments.
Data Source
AI summary
A method to select between tactical driving decisions of a controlled ego vehicle to reach a target destination is described. The method includes determining upcoming tactical driving decisions of the controlled ego vehicle to reach the target destination according to a mission plan. The method also includes ranking upcoming tactical driving maneuvers associated with each of the upcoming tactical driving decisions. The method further includes selecting a tactical driving maneuver prior to a critical intersection according to the ranking of the upcoming tactical driving maneuvers to reach the target destination according to the mission plan.