A Two-Level Control Algorithm for Autonomous Driving in Urban Environments

Erfan Aasi, Mingyu Cai, Cristian-Ioan Vasile, Calin Belta

Abstract: We propose a two-level hierarchical architecture for controlling an autonomous vehicle (ego) in complex urban driving environments. This approach ensures both collision avoidance and adherence to traffic rules while maintaining real-time performance. At the top level of our framework, we use a simple dynamic model for ego and a simplified representation of the environment to formulate a Model Predictive Control (MPC) problem. The traffic rules are represented by Signal Temporal Logic (STL) formulas and incorporated as mixed integer-linear constraints within the MPC optimization. The top level MPC solution is then simulated at the bottom level, which employs detailed models of both ego dynamics and the environment. If a collision or traffic rule violation occurs, the bottom level provides feedback to the top level in the form of correction constraints, which are mixed integer-linear constraints affecting the state and control input of ego. This closed-loop feedback from the bottom level helps address discrepancies between the simplified models used in the MPC and the complex real-world models. We assess the effectiveness and runtime performance of our method by comparing it with existing approaches, through simulations of various urban driving scenarios in the CARLA simulator.

External IDs:dblp:journals/tits/AasiCVB25