Evolving a general electronic stability program for car simulated in TORCS

Jilin Huang, I. Tanev, K. Shimohara
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引用次数: 9

Abstract

We present an approach of evolving (via Genetic Programming, GP) the electronic stability program (ESP) of a car, realistically simulated in The Open Racing Car Simulator (TORCS). ESP is intended to assist the yaw rotation of an unstable (e.g., either understeering or oversteering) car in low-grip, slippery road conditions by applying a carefully-timed asymmetrical braking forces to its wheels. In the proposed approach, the amount of ESP-induced brake force is represented as an evolvable (via GP) algebraic function (brake force function BFF) of the values of parameters, pertinent to the state of the car, and their derivatives. In order to obtain a general BFF, i.e., a function that result in a handling of the car, that is better than that of non ESP car, for a wide range of conditions, we evaluate the evolving BFF in several fitness cases representing different combinations of surface conditions and speeds of the car. The experimental results indicate that, compared to the car without ESP, the best evolved BFF of ESP offers a superior controllability - in terms of both (i) a smaller deviation from the ideal trajectory and (ii) faster average speed on a wide range of track conditions (“icy”, “snowy”, “rainy” and “dry”) and traveling speeds. Presented work could be viewed as an attempt to contribute a new functionality in TORCS that might enrich the experience of gamers by the enhanced controllability of their cars in slippery road conditions. Also, the results could be seen as a step towards the verification of the feasibility of applying GP for automated, evolutionary development of ESP.
在TORCS中模拟汽车通用电子稳定程序的发展
我们提出了一种进化(通过遗传编程,GP)汽车的电子稳定程序(ESP)的方法,在开放赛车模拟器(TORCS)中进行了逼真的模拟。ESP旨在通过对车轮施加定时的不对称制动力,在低抓地力、湿滑路面上帮助不稳定(例如转向不足或转向过度)的汽车偏航旋转。在所提出的方法中,esp诱导制动力的量被表示为与汽车状态相关的参数值及其导数的可进化代数函数(制动力函数BFF)。为了获得一个通用的BFF,即一个导致汽车处理优于非ESP汽车的函数,在广泛的条件下,我们在代表不同表面条件和汽车速度组合的几个适应度情况下评估不断变化的BFF。实验结果表明,与没有ESP的汽车相比,ESP的最佳进化BFF提供了更好的可控性——在(i)与理想轨迹的偏差较小,(ii)在广泛的轨道条件下(“结冰”、“下雪”、“下雨”和“干燥”)和行驶速度上都有更快的平均速度。目前的工作可以看作是为TORCS贡献新功能的一种尝试,通过增强汽车在湿滑道路条件下的可控性来丰富玩家的体验。此外,研究结果可以被视为验证GP应用于ESP自动化、进化开发的可行性的一步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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