Autonomous Vehicle Interaction Labeling for Trajectory Optimization
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Solution Overview
Problem
Autonomous vehicles face challenges in effectively managing interactions with other objects in dynamic environments, such as determining optimal trajectories and control commands to ensure safe navigation while considering various obstacles and their predicted trajectories.
Innovation Solution
The system employs a method to detect objects using sensors, assign interaction labels (yield, no-yield, stay-behind, and irrelevant), and generate cost functions based on these labels to determine optimal vehicle trajectories through a motion planner, which integrates sensor data, predicted object trajectories, and road topology to generate control commands for steering, gas, and brakes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the autonomous vehicle uses a motion planner to determine optimal trajectories by considering multiple objects and their interactions, then the safety and collision avoidance capability is improved, but the computational complexity and processing time increase
Solution Approach 1:
The system segments the complex interaction management by introducing interaction labels that categorize object relationships into distinct types (yield, no-yield, stay-behind, stay-in-front, irrelevant). This segmentation transforms the continuous complex interaction problem into discrete manageable categories, reducing computational complexity while maintaining safety through structured decision-making frameworks.
Solution Approach 2:
The system performs preliminary action by determining interaction labels for all detected objects before computing the cost function and trajectory. This pre-categorization of object interactions allows the motion planner to efficiently process trajectories by referencing pre-established interaction categories, reducing real-time computational burden while ensuring safety constraints are met.
2Reliability
If the autonomous vehicle determines trajectories for an interval of time based on interaction labels and cost functions, then the navigation safety is improved, but the processing time and computational resources increase
Solution Approach 1:
The system determines interaction labels for all detected objects in advance before trajectory optimization. This preliminary categorization of object interactions (yield, no-yield, stay-behind, etc.) prepares the computational framework ahead of time, allowing the motion planner to efficiently compute trajectories by referencing pre-established interaction categories rather than evaluating all possible interactions in real-time.
Solution Approach 2:
The system changes parameters by transforming continuous spatial and temporal interactions into discrete interaction label categories. This parameter transformation converts complex continuous optimization variables into discrete states (five interaction labels), significantly reducing the computational search space while maintaining navigation safety through the structured cost function formulation.
3Adaptability or versatility
If the system assigns interaction labels and generates cost functions for multiple objects, then the ability to manage complex interactions is improved, but the device complexity and algorithm complexity increase
Solution Approach 1:
The system segments complex object interactions into five distinct interaction label categories (yield, no-yield, stay-behind, stay-in-front, irrelevant). This segmentation provides a structured framework for managing diverse interaction scenarios, enhancing adaptability to different traffic situations while reducing algorithmic complexity through discrete categorization rather than continuous evaluation.
Solution Approach 2:
The interaction label framework serves as a universal mechanism that handles multiple types of object interactions through a single unified categorization system. This multi-functional approach allows the same labeling and cost function generation process to manage various interaction scenarios (pedestrians, vehicles, cyclists, stationary objects) uniformly, enhancing versatility without proportionally increasing complexity.
Data Source
AI summary
A method and a system for managing interactions of an autonomous vehicle with one or more objects are described. In one embodiment, objects that are in the vicinity of the autonomous vehicle are detected based on sensor measurements. An interaction label is determined for each one of the objects. The interaction label is one of a yield label, a no-yield label, a stay-behind label, a stay in-front label, and an irrelevant label and describes an interaction of the autonomous vehicle with the object. A cost function is generated for the object based on the interaction label. A trajectory for an interval of time is determined for the autonomous vehicle based on the cost functions of one or more objects. The trajectory is then used to generate control commands for controlling the motion of the autonomous vehicle during the interval of time.