用可微分优化提升自动驾驶避障安全性，应对传感器噪声风险。
Differentiable Optimization Layered Safety-Critical Control for Risk-Aware Navigation via Conformal Prediction
- 通过置信预测生成带风险的障碍物椭球，量化感知不确定性。
- 两层可微分优化构建避障与可行性保障的控制屏障函数。
- 适合高安全性要求的自动驾驶系统，尤其复杂城市环境应用。
在未知环境中实现风险感知导航是自动驾驶车辆在复杂城市系统中运行的核心挑战。为解决此问题，本文提出一种基于置信预测的可微分优化分层安全关键控制方法。首先，针对传感器噪声带来的不确定性，采用置信预测方法生成围绕椭圆形机器人周围的风险感知障碍物椭球。其次，引入两个嵌套的可微分优化层，分别构建用于避障和可行性保障的控制屏障函数。随后，提出基于二次规划的安全关键控制律，将上述控制屏障函数约束及输入约束统一整合。最后，通过数值仿真验证了所提框架的有效性。
原文摘要 · Abstract (English)
Risk-aware navigation in unknown environments is a fundamental challenge for autonomous vehicles operating in complex urban systems. To address this issue, this paper presents a differentiable optimization layered safety-critical control method based on conformal prediction. First, to handle uncertainties arising from sensor noise, the conformal prediction method is employed to generate risk-aware obstacle ellipsoids around an elliptical-shaped robot. Second, two nested differentiable optimization layers are introduced to build the control barrier functions for obstacle avoidance and feasibility guarantee, respectively. Then, a quadratic program based safety-critical control law is proposed to integrate the above control barrier function constraints as well as input constraints. In the end, the effectiveness of the proposed framework is demonstrated through numerical simulations.
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