遗传算法实现自动泊车技术方案

2021年，一项利用遗传算法实现自动泊车的技术方案被发布。1该方案将泊车问题转化为基因组优化任务，通过虚拟汽车的进化学习过程来探索自动泊车的可行性。1

技术实现采用180比特的基因组结构，编码18个系数，每个系数占用10比特。1初始种群包含约1000辆虚拟汽车，通过选择、交配和变异等遗传算法操作不断迭代。1在约40代进化后，虚拟汽车开始学会基本的泊车动作。1该系统配置了0.04的变异概率和6%的长寿冠军保留比例，以平衡种群多样性与优秀基因的保留。1

虚拟汽车配备8个距离传感器用于环境感知，检测范围为0至4米。1其控制系统包括发动机（控制前进、后退或停止）和方向盘（控制左转、直行或右转）两个执行器。1脑部模型采用两个线性多项式结构，各包含8个传感器变量加1个偏置项。1适应度函数基于汽车四个轮子与停车位四个角之间的欧氏距离倒数计算。1

整个系统用TypeScript编写，代码量不足500行（不含测试）。1用户可通过浏览器模拟器实时观察虚拟汽车的进化过程和泊车学习效果。1

A developer demonstrated an automated parking system trained through genetic algorithms, decomposing the parking problem into an optimization challenge for a 180-bit genome 1. The approach utilized a population of approximately 1,000 virtual vehicles that evolved over successive generations, with the cars beginning to master basic parking maneuvers after about 40 generations of evolution 1.

The system relied on eight distance sensors with a detection range of 0 to 4 meters to perceive the parking environment 1. Vehicle control was managed through two actuator types: an engine capable of moving forward, backward, or stopping, and a steering mechanism offering left, straight, or right turning options 1. The artificial brain consisted of two linear polynomial models, each incorporating eight sensor variables plus a bias term 1. Fitness was determined by calculating the reciprocal of the Euclidean distance between the car's four wheels and the corresponding corners of the parking space 1.

The implementation was developed in TypeScript with fewer than 500 lines of code, excluding test files 1. The genetic algorithm employed a mutation probability of 0.04 and retained the top 6 percent of performers across generations to preserve successful traits 1. Users could observe the evolutionary process in real time through a browser-based simulator, providing direct visualization of how virtual vehicles progressively refined their parking skills 1.