Probabilistic Safety-Assured Adaptive Merging Control for Autonomous Vehicles

TL;DR

This paper introduces a real-time probabilistic safety control framework for autonomous vehicles that interacts with human drivers during ramp merging, ensuring safety under uncertainty through a novel bi-level optimization approach.

Contribution

It extends control barrier functions to a probabilistic setting with chance constraints and develops a bi-level optimization framework for safe, adaptive merging control.

Findings

Proven probabilistic safety guarantees under motion uncertainty.

Effective real-time control in ramp merging scenarios.

Demonstrated adaptability to different driving styles.

Abstract

Autonomous vehicles face tremendous challenges while interacting with human drivers in different kinds of scenarios. Developing control methods with safety guarantees while performing interactions with uncertainty is an ongoing research goal. In this paper, we present a real-time safe control framework using bi-level optimization with Control Barrier Function (CBF) that enables an autonomous ego vehicle to interact with human-driven cars in ramp merging scenarios with a consistent safety guarantee. In order to explicitly address motion uncertainty, we propose a novel extension of control barrier functions to a probabilistic setting with provable chance-constrained safety and analyze the feasibility of our control design. The formulated bi-level optimization framework entails first choosing the ego vehicle's optimal driving style in terms of safety and primary objective, and then minimally modifying a nominal controller in the context of quadratic programming subject to the probabilistic safety constraints. This allows for adaptation to different driving strategies with a formally provable feasibility guarantee for the ego vehicle's safe controller. Experimental results are provided to demonstrate the effectiveness of our proposed approach.