Risk-Biased Trajectory Forecasting for Long-Tail Driving Risk
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Solution Overview
Problem
Current autonomous vehicle technologies face challenges in accurately predicting risk in complex traffic environments, leading to overconfident and unsafe behavior due to underestimation of long-tail safety-critical events in finite-sampling approximations of probabilistic motion forecasts.
Innovation Solution
A risk-biased trajectory forecasting method that augments a pre-trained generative model with an additional encoding process to deliberately overestimate the probability of dangerous trajectories, providing distributional robustness against inaccuracies in human behavior models, and allowing for accurate risk estimation in real-time with reduced prediction samples.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If finite-sampling approximations of probabilistic motion forecasts are used, then computational efficiency is improved, but risk estimation accuracy deteriorates due to underestimation of long-tail safety-critical events
Solution Approach 1:
The patent changes the parameter of probability distribution by introducing a risk-biased distribution that deliberately overestimates the probability of dangerous trajectories. This is achieved by modifying the sampling process to focus on high-risk regions of the trajectory space, thereby improving risk estimation accuracy without requiring exhaustive sampling of all possible trajectories.
Solution Approach 2:
The patent performs preliminary risk assessment by pre-identifying and weighting potentially dangerous trajectories before final prediction. The risk-biased sampling approach proactively focuses computational resources on trajectories that are more likely to be safety-critical, rather than treating all trajectories equally during the prediction process.
2Device complexity
If risk-neutral trajectory forecasting is used, then model simplicity is maintained, but safety performance deteriorates due to overconfident and unsafe autonomous agent behavior
Solution Approach 1:
The patent modifies the probability distribution parameters in the trajectory forecasting model to create a risk-biased distribution. This changes the model's behavior from risk-neutral to risk-aware by adjusting the weights assigned to different trajectories based on their potential safety implications, without fundamentally changing the model architecture.
Solution Approach 2:
The patent introduces a risk-biasing mechanism as an intermediary layer between the base forecasting model and the decision-making system. This intermediary adjusts the probability estimates to account for safety risks, allowing the simple base model to be enhanced with risk awareness without requiring complex modifications to the core forecasting architecture.
Data Source
AI summary
A method of forecasting risk-biased trajectories of agents surrounding an ego vehicle is described. The method includes sampling a risk-neutral latent space generated by a trained encoder of a generative network based on past surrounding agent trajectories. The method also includes predicting, based on the sampling of the risk-neutral latent space, risk-neutral future surrounding agent trajectories using a trained decoder of the generative network. The method further includes sampling a risk-biased latent space distribution generated by a trained, risk-aware encoder of the generative network based on past trajectories of the ego vehicle and a risk-sensitivity. The method also includes predicting, based on the sampling of the risk-biased latent space distribution, risk-biased future surrounding agent trajectories using the trained decoder of the generative network.