End-to-End Vehicle Perception Model Training for Tracking Accuracy
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Solution Overview
Problem
Autonomous vehicle perception systems face challenges in accurately tracking objects and making predictions due to occlusions and data switching, as they typically train object detection and tracking models separately without considering temporal information.
Innovation Solution
The system trains a machine-learned model end-to-end, incorporating temporal data to associate detected objects with stored tracking data, improving the accuracy of object tracking and prediction by using outputs from one model portion as inputs for another, thereby enhancing overall perception system performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If object detection and tracking models are trained separately, then training complexity is reduced and ease of manufacture is improved, but tracking accuracy deteriorates due to occlusions and data switching
Solution Approach 1:
The patent combines separate object detection and tracking models into a unified end-to-end trained system. The detection model and tracking model are integrated such that detection outputs directly feed into tracking inputs, allowing joint optimization of both functions. This merging resolves the contradiction by achieving high tracking accuracy through unified training while maintaining practical implementability through modular architecture.
Solution Approach 2:
The system performs preliminary object detection before tracking by using the detection model to identify objects and generate bounding boxes in advance. These detection results serve as preprocessed inputs for the tracking model, enabling the tracking system to focus computational resources on maintaining accurate trajectories rather than detecting objects from scratch. This preliminary action improves tracking accuracy while keeping the overall system manageable.
2Device complexity
If temporal information is not incorporated in separate models, then model complexity is reduced, but prediction accuracy deteriorates due to occlusions and data switching
Solution Approach 1:
The patent incorporates temporal information by adding a time dimension to the tracking model inputs. The system processes sequences of detection results over time, using temporal patterns to maintain tracking accuracy during occlusions. This dimensional extension enables the model to predict object trajectories and handle data switching without excessively increasing architectural complexity, as the temporal processing leverages standard sequence modeling techniques.
3Measurement precision
If end-to-end training is implemented, then tracking accuracy is improved by minimizing occlusions and data switching, but training time and computational resources increase
Solution Approach 1:
The unified system is segmented into distinct detection and tracking portions that can be trained separately before being combined for end-to-end fine-tuning. This segmentation allows the system to benefit from end-to-end training accuracy improvements while reducing total training time by avoiding repeated full-system training. The modular structure enables efficient resource utilization during the training process.
Data Source
AI summary
Techniques for a perception system of a vehicle that can detect and track objects in an environment are described herein. The perception system may include a machine-learned model that includes one or more different portions, such as different components, subprocesses, or the like. In some instances, the techniques may include training the machine-learned model end-to-end such that outputs of a first portion of the machine-learned model are tailored for use as inputs to another portion of the machine-learned model. Additionally, or alternatively, the perception system described herein may utilize temporal data to track objects in the environment of the vehicle and associate tracking data with specific objects in the environment detected by the machine-learned model. That is, the architecture of the machine-learned model may include both a detection portion and a tracking portion in the same loop.