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近年来,智能化革命席卷全球,以深度学习为核心的AI技术取得了重大突破。在机器人[1]、语音识别[2-3]、图像识别[4-7]、自然语言处理[8-9]等多个任务上,人工智能技术的识别能力和决策水平已经追平甚至超越人类,如以AlphaGo为代表的人工智能机器人击败人类职业围棋冠军,以Google、百度等工业界为代表的无人驾驶汽车已经开始实际道路上路测试等。
美国斯坦福大学尼尔逊教授将人工智能定义为:“人工智能是关于知识的学科—怎样表示知识以及怎样获得知识并使用知识的科学”[10]。麻省理工学院温斯顿教授认为:“人工智能就是研究如何使计算机去做过去只有人才能做的智能工作”。这些说法[10-11]反映了人工智能学科的基本思想和基本内容,以及其他诸多对人工智能的理解[12-14]。这些观点都反应了人工智能是通过研究人类智能活动规律,构造具有一定智能的人工系统,研究如何应用计算机的软硬件技术来模拟和代替人类某些智能行为(如学习、推理、思考、规划、控制等)的基本理论、方法和技术。
人工智能技术的发展受到了广泛的重视,并在机器人、控制系统中得到了广泛应用,为传统制造业提供了前所未有的发展机遇。汽车行业作为传统制造业的龙头之一,也立足自身发展,结合智能化技术,展开了传统汽车面向智能化革新的进程。智能汽车以汽车为载体,应用一系列高精尖信息化技术和智能化技术(传感器感知技术、V2X网联通讯技术、驾驶决策技术等),即代表了汽车技术产业化进程的重要方向,也是汽车技术创新发展的主流趋势。我国工信部把智能汽车定义为:搭载先进的车载传感器、控制器、执行器等装置,并融合现代通讯与网络技术,实现车与车、人、路、云等智能信息的交换、共享,具备复杂环境感知、智能决策、协同控制等功能,可实现安全、高效、舒适、节能行驶,并最终可实现代替人来操作的新一代汽车。
智能汽车中的智能化技术,可分为3个模块:环境感知层、决策规划层和运动控制层。环境感知层利用环境感知传感器(视觉传感器、激光雷达、毫米波雷达、超声波雷达、里程计、GPS等)感知车辆行驶环境信息,利用车辆自身状态传感器(如轮速检测等)感知车辆自身状态。经过智能化模型处理后,感知出车辆周围环境(如绝对位置、车道线、周围车辆相对位置、行人位置、动态静态障碍物类型和位置、行为预测等),决策规划层按照驾驶决策算法将空间、时间上的独立信息、互补信息和冗余信息进行理解,根据实时感知到的车辆周围环境信息,实时决策车辆可执行的驾驶指令并规划出行程轨迹。运动控制层接收决策规划层的驾驶指令,控制车辆稳定运行的同时保证车辆的控制精度。
随机性和模糊性导致不确定性是人类思维活动中最基本的特性。对人类思维模拟、研究的人工智能技术,也具有不确定性的特点。随着科学技术不断深入发展,需要学者们研究的变量越来越多,而且变量之间的关系也越来越复杂,对系统的判别和推理的精确性要求也越来越高。实践告诉我们:复杂的系统往往难以精确化。这就使得人们对系统精确性的需求和问题本身的复杂性之间形成矛盾。复杂性越高,有意义的精确化能力就越低,而复杂性意味着因素众多,使人们在求解这类复杂问题时,只能抓住问题的主要部分,忽略次要部分,而这又常常使本身明确的概念变得模糊起来,从而导致不确定性。
因人为主观因素导致的安全问题,往往通过政府部门健全法律、法规,引导、管理人工智能技术健康发展,本文将焦点放在因客观技术问题引起的安全问题方面。目前,智能车的安全性问题越来越受到社会重视。国务院于2017年发布的《新一代人工智能发展规划》中明确指出:“在大力发展人工智能的同时,必须高度重视可能带来的安全风险挑战,加强前瞻预防与约束引导,最大限度降低风险,确保人工智能安全、可靠、可控发展”[15]。基于上述原因,预期功能安全(safety of the intended functionality, SOTIF)的研究应运而生。预期功能安全在ISO/PAS 21448中首次给出定义[16],关注由功能不足或者由可合理预见的人员误用所导致的危害和风险。例如,传感系统在暴雨、积雪等天气情况下,传感器本身功能未发生故障,但智能车是否仍能按预期行驶。
本文总结智能汽车研究中的环境感知算法、智能决策算法、智能化算法的不确定性以及不确定性带来的安全问题等4个方面的研究情况,以期引起相关研究者的关注并提供指导。
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