Range Sensor Prediction for Autonomous Obstacle Avoidance
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Solution Overview
Problem
Autonomous vehicles face challenges in predicting future sensory observations to create optimized trajectories and avoid obstacles, as existing methods are complex and may be sensitive to perturbations, especially when relying on image data.
Innovation Solution
A method using a temporal neural network that processes sequences of previous sensory observations and control actions to predict future sensory observations, incorporating a gated recurrent unit and multi-layer perceptrons, which enhances environmental perception and obstacle detection, making it less sensitive to perturbations and adaptable for active or passive range sensing devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods use image data for environmental perception, then obstacle detection capability is improved, but sensitivity to perturbations increases and computational complexity increases
Solution Approach 1:
The patent replaces image data processing with range sensing data (ultrasonic, lidar, radar) for environmental perception. This substitution of the sensing modality reduces sensitivity to image perturbations while maintaining obstacle detection capability, as range data provides direct distance measurements that are more robust to environmental variations.
Solution Approach 2:
The patent changes the parameter space from image coordinates to range measurements. By transforming the perception task from processing pixel data to processing distance measurements, the system achieves better reliability while maintaining detection precision through the use of temporal neural networks on range sensing sequences.
2Measurement precision
If complex deep learning models are used for trajectory optimization, then prediction accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent segments the prediction task into multiple temporal steps, where the temporal neural network predicts future range measurements step-by-step. This segmentation allows the use of a relatively simple recurrent architecture that processes sequences efficiently, achieving good prediction accuracy without requiring computationally intensive complex models.
Solution Approach 2:
The temporal neural network uses its own previous predictions as inputs for subsequent predictions, creating a self-service predictive mechanism. This recursive prediction approach allows the system to maintain accurate multi-step predictions while using a computationally efficient recurrent architecture rather than complex models.
Data Source
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AI summary
The present invention is related to a method, a computer program code, and an apparatus for predicting future sensory observations of a distance ranging device of an autonomous or semi-autonomous vehicle. The invention is further related to an autonomous driving controller using such a method or apparatus and to an autonomous or semi-autonomous vehicle comprising such an autonomous driving controller. In a first step, a sequence of previous sensory observations and a sequence of control actions are received (S1). The sequence of previous sensory observations and the sequence of control actions are then processed (S2) with a temporal neural network to generate a sequence of predicted future sensory observations. Finally, the sequence of predicted future sensory observations is output (S3) for further use.