Problem
Research questions and friction points this paper is trying to address.
Model Predictive Control
autonomous car racing
driving context
Driving Context-guided Model Predictive Planning and Control for Autonomous Car Racing at the Limit and Beyond
🤖 AI Summary
本文提出一种基于模型预测控制的自动驾驶赛车运动规划与控制方法，通过成本混合状态机适应不同驾驶情境，实现极限驾驶和超车等操作。
📝 Abstract
This paper presents a Model Predictive Control-based motion planning and control pipeline for autonomous car racing capable of adapting to different driving contexts, such as overtaking, nominal driving, and countersteering. A Cost Blending state machine manages the identification of different driving contexts and the selection of their predefined weights to be applied to the Model Predictive Planning (MPP) and Control (MPC) modules. The two optimization-based solutions share the same problem formulation and model, differing only in horizon length, rate, tuning, and in their open-loop versus closed-loop approach to maximize the effectiveness of their interaction. The work is validated on the fully autonomous open-wheel racecar Superformula EAV-25, with a lap time achieved that is within 2% of the best human driver reference. The results demonstrate the capability of the solution in driving at the limit of handling, smoothly executing overtaking maneuvers, and quickly reacting to high oversteering conditions to recover the vehicle stability.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Model Predictive Control
Cost Blending
Driving Contexts
Autonomous Car Racing
Overtaking Maneuvers
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