Lidar 3D Detection and Forecasting Without Tracking Error Propagation
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Solution Overview
Problem
Conventional autonomous vehicle navigation systems face challenges in predicting object behavior due to computational errors in perception stacks and managing processing bandwidth, particularly with lidar-based systems, which lead to inaccurate forecasting and increased computational redundancies.
Innovation Solution
A system that eliminates the tracking phase in lidar data processing, using a learned model to generate object trajectories directly from 3D point clouds, reducing errors and computational redundancies by converting 3D data to 2D representations and processing only necessary frames, thereby improving prediction accuracy and processing efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a stack of lidar sweeps is processed through detection, tracking, and prediction steps independently, then object detection and trajectory forecasting are achieved, but computational errors propagate through the pipeline and reduce forecasting accuracy
Solution Approach 1:
The patent merges detection, tracking, and prediction into a unified neural network model that processes lidar sweeps end-to-end. This integration eliminates the independent pipeline steps that cause error propagation, allowing the system to learn optimal representations directly from raw sensor data without intermediate processing errors accumulating.
Solution Approach 2:
The patent introduces a novel intermediate representation called 'spatiotemporal cones' that serves as a bridge between raw lidar data and trajectory predictions. This intermediary structure efficiently captures object motion patterns while reducing computational complexity, acting as a mediator that prevents error propagation while maintaining forecasting accuracy.
2Productivity
If detectors treat every frame in a sequence of lidar sweeps independently, then each frame is processed completely, but large data overlaps occur and computational redundancies increase significantly
Solution Approach 1:
The patent performs preliminary processing by converting lidar sweeps into spatiotemporal cone representations that capture essential motion information. This preliminary transformation allows subsequent frames to be processed more efficiently by leveraging temporal relationships already encoded in the cone structures, reducing redundant computations across frames.
Solution Approach 2:
The patent implements dynamic processing where the system adapts its computational approach based on detected object motion patterns. By using learned models that identify temporal dependencies, the system dynamically adjusts which frames require full processing versus which can use simplified processing, reducing overall computational redundancy while maintaining accuracy.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach enhances prediction accuracy and reduces processing time and bandwidth, enabling faster decision-making and improved safety for autonomous vehicles by minimizing errors and computational overhead.
Implementation Method 1
Individual points are measured by generating a laser pulse and detecting a returning pulse, if any, reflected from an environmental object, and determining the distance to the reflective object according to the time delay between the emitted pulse and the reception of the reflected pulse
Implementation Method 2
One such sensor is a light detection and ranging (lidar) device
Data Source
AI summary
Disclosed herein are system, method, and computer program product aspects for enabling an autonomous vehicle (AV) to detect objects and forecast their predicted positions. The system can monitor an object within a vicinity of the AV. A plurality of trajectories predicting paths the object will take at a future time can be generated, the plurality of trajectories being based on a generated three-dimensional (3D) point cloud map indicating current and past characteristics of the object. Using a learned model, a forecasted position of the object at an instance in time can be generated along one or more of the plurality of trajectories. A maneuver for the AV can be performed based on the forecasted position.