IteraOptiRacing: A Unified Planning-Control Framework for Real-time Autonomous Racing for Iterative Optimal Performance

--This paper presents a unified planning-control strategy for competing with other racing cars called IteraOptiRacing in autonomous racing environments. This unified strategy is proposed based on Iterative Linear Quadratic Regulator for Iterative T asks (i2LQR), which can improve lap time performance in the presence of surrounding racing obstacles. By iteratively using the ego car's historical data, both obstacle avoidance for multiple moving cars and time cost optimization are considered in this unified strategy, resulting in collision-free and time-optimal generated trajectories. The algorithm's constant low computation burden and suitability for parallel computing enable real-time operation in competitive racing scenarios. T o validate its performance, simulations in a high-fidelity simulator are conducted with multiple randomly generated dynamic agents on the track. Results show that the proposed strategy outperforms existing methods across all randomly generated autonomous racing scenarios, enabling enhanced maneuvering for the ego racing car . A. Motivation Recently, there has been a growing interest in autonomous racing [1]-[4], which is a challenging subtopic in the field of autonomous driving research. In such racing competitions, the ego vehicle is expected to complete the required number of laps on a designated track in the shortest time possible. To achieve this goal, the autonomous racing algorithm must address two critical challenges: maximizing driving speed while simultaneously competing with other cars on the same track. Traditionally, most existing work in this area tackles these two problems separately. However, to secure victory in a race, an algorithm must deliver time-optimal behavior in the presence of other competing dynamic vehicles. In response to this need, we propose a racing algorithm that enables the ego vehicle to maintain high-speed performance even in the presence of surrounding competing vehicles by considering global optimality, as shown in Figure 1.