Adaptive Trajectory Collision Checking for Autonomous Vehicles
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Solution Overview
Problem
Existing methods for generating routes for autonomous vehicles are computationally intensive and may not provide safe or comfortable routes for passengers, as they do not effectively adapt to varying environmental conditions such as curvature and obstacle proximity.
Innovation Solution
The techniques involve adaptively scaling the density of trajectory points based on cost-associated factors like curvature and obstacle proximity, using a higher density in high-activity areas and lower density in low-activity areas, and dynamically adjusting weights to prioritize safety and comfort by classifying objects and vehicle velocity.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If uniform high density of trajectory points is used throughout the route, then route accuracy and safety are improved, but computational burden increases significantly
Solution Approach 1:
The patent applies local quality by varying the density of trajectory points based on the local environmental characteristics. High-density points are concentrated in high-activity areas (near obstacles, sharp curvatures) where precision is critical for safety, while low-density points are used in low-activity areas (open spaces, straight paths) where computational resources can be reduced. This resolves the contradiction by making precision local rather than uniform.
Solution Approach 2:
The patent implements dynamics by adaptively adjusting the trajectory point density during route generation based on real-time environmental assessment. The system dynamically identifies high- and low-activity regions and allocates computational resources accordingly, allowing the trajectory representation to be flexible and responsive to changing environmental conditions rather than static and uniform.
2Device complexity
If uniform trajectory point density is used, then implementation simplicity is maintained, but safety and comfort in varying environmental conditions deteriorate
Solution Approach 1:
The patent changes the parameter of trajectory point density from a fixed uniform value to a variable parameter that adapts to environmental conditions. By modifying this parameter based on activity levels (obstacle proximity, curvature), the system achieves better safety and comfort without requiring complex structural changes to the overall trajectory generation framework.
3Productivity
If computational resources are reduced by using lower trajectory point density, then processing speed improves, but route accuracy in critical areas may deteriorate
Solution Approach 1:
The patent ensures that route accuracy is maintained in critical areas by concentrating trajectory points locally where needed (near obstacles, sharp turns) while reducing density in non-critical areas. This local quality approach preserves measurement precision where it matters most for safety while achieving overall processing speed improvements.
Data Source
AI summary
Techniques for generating trajectories and drivable areas for navigating a vehicle in an environment are discussed herein. The techniques can include receiving a trajectory representing an initial trajectory for a vehicle, such as an autonomous vehicle, to traverse the environment in a drivable area. A location can be determined along the trajectory. A cost associated with the location can determined and can be evaluated with respect to a cost threshold. Further, the techniques can include determining, based at least in part on the cost meeting or exceeding the cost threshold, an action associated with the location, and controlling the autonomous vehicle to traverse the environment based at least in part on the action.