Risk aggregation method based on improved conditional value-at-risk in cross-country scene

By using an improved conditional value at risk (VaR) method and sparse Gaussian process regression, a comprehensive access risk map for off-road scenarios is constructed, which solves the problems of fragmentation and uncertainty in risk assessment in off-road autonomous driving and achieves high-precision and robust risk assessment and path planning.

CN122087690APending Publication Date: 2026-05-26BEIJING INST OF TECH
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Patent Information

Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
BEIJING INST OF TECH
Filing Date
2025-12-31
Publication Date
2026-05-26

AI Technical Summary

Technical Problem

Existing technologies for assessing the risks of off-road terrain traversal in autonomous driving suffer from fragmented assessment standards and a lack of systematic integration. They are unable to accurately capture low-probability, high-loss extreme risk events and do not fully consider the uncertainty of perception data, leading to a decline in the reliability of risk assessment.

Method used

An improved conditional value at risk approach is adopted. By establishing a multi-level coordinate system transformation relationship and combining it with the sparse Gaussian process regression method, a 2.5D raster elevation map is constructed. Risk indicators such as flatness, slope and terrain difference are integrated and risk weights are optimized to generate a comprehensive access risk map.

Benefits of technology

It significantly improves the modeling accuracy and robustness of traffic risk assessment in off-road environments, enhances the physical rationality and local adaptability of risk assessment, and improves the ability to identify low-probability, high-loss terrain, providing a reliable foundation for real-time autonomous path planning.

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Abstract

The invention relates to a risk aggregation method based on improved conditional value-at-risk in an off-road scene, belongs to the technical field of vehicle control, and solves the problem that off-road environment traffic risks are difficult to accurately and reliably assess due to the fact that risk indexes are isolated analysis or simple fusion and uncertainty of data is not fully considered in the prior art. Comprising the following steps: establishing a conversion relation among a planning coordinate system, a vehicle body coordinate system and a sensor coordinate system by taking a world coordinate system as a reference; acquiring current pose information of the vehicle and current point cloud data of the sensor in real time; performing horizontal processing on the current point cloud data to obtain horizontal point cloud data, and constructing a 2.5 D grid elevation map by adopting a sparse Gaussian process regression method; obtaining the cost of each traffic risk index of each grid based on a 2.5 D grid elevation map; and converting the cost of each traffic risk index to a planning coordinate system, and generating a current comprehensive traffic risk map by adopting an improved conditional value-at-risk method.
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