無人駕駛汽車的不斷普及導緻了自主賽車領域的研究和發展，而自主賽車中的超車是一項具有挑戰性的任務。車輛必須在動态操控的極限下進行檢測和操作，車内的決策必須在高速和高加速度下做出。自主競賽中最關鍵的部分之一是路徑規劃和決策，以便與動态對手車輛進行超車操作。在本文中，我們介紹了對基于賽道的自主賽車離線政策學習方法的評估。我們定義了特定的賽道部分，并進行了離線實驗，以評估基于自我車輛的速度和位置的超車演習的機率。基于這些實驗，我們可以為每個賽道部分定義超車的機率分布。此外，我們提出了一個切換式MPCC控制器設定，以納入所學到的政策，實作更高的超車機動率。通過詳盡的模拟，我們表明，我們提出的算法能夠增加不同軌道部分的超車數量。
A. 自主賽車
近幾年來，自動駕駛賽車已經變得很流行，像Roborace[1]或Indy Autonomous Challenge這樣的比賽，以及像F1Tenth[2]這樣的小型賽車，為評估自動駕駛軟體提供平台。所有這些比賽的總體目标是，研究人員和工程師可以開發在車輛邊緣運作的算法。高速、高加速、高計算能力、對抗性環境。與一級方程式賽車等正常的賽車系列類似，自主賽車的算法開發在自主駕駛領域産生了信任，并使先進的自主駕駛算法得到發展。到目前為止，在自主賽車領域開發的算法大多隻關注單車，試圖實作類似人類的單圈時間。到目前為止，與動态對手的高動态超車動作領域的展示較少。此外，實作類似人類的行為（例如，像一級方程式賽車手），對超車動作作出決定，并在高速下執行安全可靠的動作，仍然是一個未解決的問題。
原文标題：Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars
原文：
The rising popularity of driver-less cars has led to the research and development in the field of autonomous racing, and overtaking in autonomous racing is a challenging task. Vehicles have to detect and operate at the limits of dynamic handling and decisions in the car have to be made at high speeds and high acceleration. One of the most crucial parts in autonomous racing is path planning and decision making for an overtaking maneuver with a dynamic opponent vehicle. In this paper we present the evaluation of a track based offline policy learning approach for autonomous racing. We define specific track portions and conduct offline experiments to evaluate the probability of an overtaking maneuver based on speed and position of the ego vehicle. Based on these experiments we can define overtaking probability distributions for each of the track portions. Further, we propose a switching MPCC controller setup for incorporating the learnt policies to achieve a higher rate of overtaking maneuvers. By exhaustive simulations, we show that our proposed algorithm is able to increase the number of overtakes at different track portions.
- Autonomous Racing
Autonomous racing has become popular over the recent years and competitions like Roborace [1] or the Indy Autonomous Challenge as well as with small-scale racecars like F1Tenth [2] provide platforms for evaluating autonomous driving software. The overall goal of all these competitions is that researchers and engineers can develop algorithms that operate at the vehicles edge: High speeds, high accelerations, high computation power, adversarial environments. Similar to normal racing series like Formula 1 the development of algorithms for autonomous racing generate trust in the field of autonomous driving and enables the development of advanced autonomous driving algorithms. The algorithms that were developed in the field of autonomous racing so far are mostly focusing on single vehicle only that try to achieve a human-like lap time. The field of high dynamic overtaking maneuver with dynamic opponents was less displayed so far. In addition, achieving a human-like behavior (e.g. like a Formula 1 race driver) that makes the decision about an overtaking maneuver and executes a secure and reliable maneuver at high speeds is still an unsolved question.
Track based Offline Policy Learning for Overtaking Maneuvers with Autonomous Racecars.pdf