Autonomous Vehicle Object Interaction Prediction for Trajectory Planning
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Solution Overview
Problem
Autonomous vehicles face challenges in accurately predicting the future locations of objects within their surrounding environment due to complex interactions with other objects, traffic rules, and their own motion, which affects their navigation and safety.
Innovation Solution
A computing system that uses sensors and machine-learned models to predict the trajectories of objects by analyzing initial trajectories, interactions, and environmental data, including traffic rules, to output predicted trajectories and inform motion planning, thereby enhancing navigation and safety.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the autonomous vehicle uses basic sensor data and simple prediction models, then the computational load is low, but the accuracy of object trajectory prediction deteriorates due to complex interactions and environmental factors
Solution Approach 1:
The prediction system is segmented into multiple independent modules: interaction detection module that identifies object interactions, trajectory prediction module that generates predicted paths, and modification module that adjusts trajectories based on interactions. This segmentation allows each module to specialize in specific tasks, improving overall prediction accuracy while maintaining manageable computational complexity through modular processing
Solution Approach 2:
The system performs preliminary interaction detection before final trajectory prediction. By first identifying potential interactions between objects (such as pedestrians near roads or vehicles in merging zones) and then applying interaction-specific modifications to predicted trajectories, the system prepares and processes data in advance, improving accuracy without overwhelming computational resources during critical real-time operation
2Reliability
If the system accounts for all object interactions and environmental factors, then the safety and navigation accuracy improve, but the computational processing time increases
Solution Approach 1:
The system applies different levels of analysis to different spatial zones: high-interaction zones (near roads, intersections, pedestrian areas) receive comprehensive interaction detection and trajectory modification, while low-interaction zones use simpler prediction models. This localized quality adjustment ensures safety-critical areas receive full analytical treatment while reducing computational overhead in safer areas, balancing reliability and processing time
Solution Approach 2:
The system dynamically adjusts prediction parameters such as time horizons, interaction detection thresholds, and trajectory modification intensity based on environmental context and object types. By changing parameters adaptively rather than applying fixed comprehensive analysis to all objects, the system maintains high safety standards for critical scenarios while reducing processing time for routine situations
Data Source
AI summary
Systems and methods for determining object motion and controlling autonomous vehicles are provided. In one example embodiment, a computing system includes processor(s) and one or more tangible, non-transitory, computer readable media that collectively store instructions that when executed by the processor(s) cause the computing system to perform operations. The operations include obtaining data associated with a first object and one or more second objects within a surrounding environment of an autonomous vehicle. The operations include determining an interaction between the first object and the one or more second objects based at least in part on the data. The operations include determining one or more predicted trajectories of the first object within the surrounding environment based at least in part on the interaction between the first object and the one or more second objects. The operations include outputting data indicative of the one or more predicted trajectories of the first object.