Multi-Sensor Trajectory Prediction Using Object Hypothesis Tracking
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing methods for predicting vehicle trajectories in autonomous driving are limited by reliance on single sensor modalities and lack an end-to-end learning approach that effectively utilizes sensor-specific information, leading to incomplete or inaccurate predictions.
Innovation Solution
An end-to-end learning-based method that integrates data from multiple sensor modalities (camera, radar, Lidar, ultrasound) to generate object hypotheses, merge and track these hypotheses over time, and predict trajectories using latent encodings, enabling simultaneous use of relevant sensor information for improved prediction accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If single sensor modality is used for trajectory prediction, then device complexity is reduced, but measurement precision and prediction accuracy deteriorate
Solution Approach 1:
The patent combines multiple sensor modalities (camera, radar, Lidar, ultrasound) into a unified trajectory prediction system. Each sensor type detects different aspects of the environment, and their data is merged through object hypothesis generation and clustering to create comprehensive trajectory predictions, thereby improving measurement precision while managing device complexity through integrated processing.
Solution Approach 2:
The system implements a universal processing framework that handles multiple sensor modalities through a common architecture. The object hypothesis generation, clustering, and trajectory prediction modules process data from various sensor types uniformly, allowing the system to leverage the strengths of each sensor modality while maintaining a manageable device complexity through standardized processing pipelines.
2Speed
If sensor-specific information is not fully utilized, then processing speed is improved, but loss of information increases
Solution Approach 1:
The system extracts sensor-specific information through object hypothesis generation for each sensor modality before merging. This extraction process captures unique characteristics from each sensor type (e.g., visual features from camera, range data from Lidar) and preserves them in the hypothesis structure, ensuring that valuable sensor-specific information is retained while enabling efficient parallel processing.
Solution Approach 2:
The processing pipeline is segmented into distinct stages: individual sensor processing, object hypothesis generation, clustering, and trajectory prediction. This segmentation allows each stage to process information at appropriate granularity, maintaining processing speed by avoiding unnecessary computations while preserving sensor information through dedicated processing steps for each modality.
3Loss of time
If traditional multi-stage training is used, then training time is increased, but model accuracy is improved
Solution Approach 1:
The patent merges multiple training stages into a single end-to-end training process. Instead of separately training object detection, tracking, and trajectory prediction modules, the system trains all components jointly as an integrated model. This combination reduces training time by eliminating multiple sequential training phases while maintaining prediction reliability through coordinated optimization of all modules simultaneously.
Data Source
AI summary
Trajectories of objects in the surroundings of a vehicle are predicted using raw sensor data of the surroundings of the vehicle detected by environment sensors and are pre-processed in a plurality of successive time intervals in order to generate object hypotheses. Based on the object hypotheses, the raw sensor data is segmented and allocated to the respective object hypothesis. The raw sensor data belonging to the respective object hypothesis is converted into latent encodings and allocated to the respective object hypothesis as a feature. Object hypotheses merged by learning-based clusters are generated from the individual object hypotheses and the allocated features. Tracks of the respective merged object hypotheses are formed by learning-based allocations between the merged object hypotheses determined in a current time interval and the merged object hypotheses ascertained in several previous time intervals, being generated. Trajectories are predicted by the tracks for the respective merged object hypotheses.