对于自动驾驶汽车来说，最难学习的课程，是那些甚至能考验最经验丰富的人类驾驶员技能的场景：比如闯红灯的骑车人突然冲出，或是动物从拐角处猛冲到迎面而来的车流中。
For autonomous vehicles, the hardest lessons to learn are the ones that can test the skills of even the most experienced human drivers: an errant cyclist darting through a red light, or an animal bolting into oncoming traffic from behind a corner.
中国科技巨头百度开发了一款新的人工智能驱动模拟器，用于让其阿波罗自动驾驶出租车应对这类罕见但风险极高的道路危险，该公司希望借此在快速增长的自动驾驶出租车市场中占据优势。
The Chinese tech giant Baidu has developed a new AI-driven simulator to prepare its Apollo robotaxis for just such rare but high-stakes road hazards, as the firm looks to gain an edge in the fast-growing self-driving taxi market.
据高管介绍，该系统名为阿波罗世界，可以从零开始构建完整的虚拟行程，让公司能够为在现实世界测试中难以安全捕捉的场景做好准备。
The system, Apollo World, can build complete virtual journeys from scratch, allowing the company to prepare vehicles for scenarios that are difficult to capture safely in real-world testing, according to executives.
周二，百度智能驾驶集团展示了新的“世界模型”，强调模拟器如何实时响应车辆的转向、制动和加速，生成同步的摄像头图像和激光雷达数据。
On Tuesday, Baidu’s Intelligent Driving Group showcased the new “world model”, highlighting how the simulator reacts in real time to the vehicle’s steering, braking and acceleration, generating synchronised camera images and lidar data.
“我们希望我们的世界模型能够从模拟单一场景，进化到模拟整个任务的执行过程，”该集团首席架构师兼世界模型负责人杨泽表示。
“We hope our world model can evolve from simulating a single scenario to simulating the execution of an entire task,” said Yang Ze, a distinguished architect and head of world models at the group.
在技术演示中，杨泽展示了阿波罗世界生成高风险危险场景，包括骑车人穿过路口、狗跑入自动驾驶出租车行驶路径等情况。工程师可以调整虚拟道路使用者的行为，以在各种条件下对驾驶软件的响应进行压力测试。
In technical demonstrations, Yang showed Apollo World generating high-risk hazards, including cyclists cutting across intersections and dogs running into a robotaxi’s path. Engineers could tweak the behaviours of virtual road users to stress-test the driving software’s responses across varied conditions.
该平台是百度推进跨境道路网络的关键。在演示中，该公司模拟了在深圳和香港的虚拟行程，交通密度不断变化。
The platform is key to Baidu’s push into cross-border road networks. During its demo, the company simulated virtual trips in Shenzhen and Hong Kong under changing traffic densities.
百度智能驾驶集团首席架构师兼阿波罗技术委员会主席王亮表示，让自动驾驶出租车适应不同地理环境，过去意味着手动硬编码当地交通规则——这是一个繁琐的过程，限制了可扩展性。
Adapting robotaxis to diverse geographies used to mean manually hardcoding local road rules – a cumbersome process that limited scalability, said Wang Liang, chief architect at Baidu’s Intelligent Driving Group and chairman of the Apollo technical committee.
王表示，不同城市的道路状况往往差异很大，他以重庆密集的多层路网、伦敦和香港的靠左行驶，以及迪拜司机激进的驾驶风格为例。
Road conditions in different cities were often highly different, Wang said, pointing to Chongqing’s dense, multi-tiered road network, left-hand driving in London and Hong Kong, and Dubai motorists’ aggressive driving style.
百度的下一代架构转而依赖端到端数据学习，训练模型将感知输入转化为驾驶动作。据杨称，其自动驾驶出租车车队收集的真实世界数据会被反馈到Apollo World中，以改进该系统。
Baidu’s next-generation architecture instead relies on end-to-end data learning, training models to translate sensory inputs into driving actions. Real-world data gathered by its robotaxi fleet is fed back into Apollo World to improve the system, according to Yang.
这家总部位于北京的公司所开展的项目，反映了全球自动驾驶行业向AI生成的训练环境转变的更广泛趋势。
The Beijing-based company’s project reflects a broader shift across the global autonomous driving sector towards AI-generated training environments.
在美国，谷歌母公司Alphabet旗下的Waymo于2月推出了基于Google DeepMind的Genie 3构建的世界模型，生成了旧金山金门大桥积雪、车辆逆行等危险场景的激光雷达和摄像头数据流。
In the US, Alphabet-owned Waymo introduced a world model in February built on Google DeepMind’s Genie 3, generating lidar and camera streams of dangerous conditions such as snow on San Francisco’s Golden Gate Bridge and cars driving on the wrong side of the road.
6月，芯片制造商英伟达展示了OmniDreams，这是一个旨在实时交互式评估自动驾驶软件的世界模型。
In June, chipmaker Nvidia presented OmniDreams, a world model designed to evaluate autonomous driving software interactively in real time.
“自动驾驶的最终目标是安全地将乘客从任意A点送到任意B点，”百度的杨表示，“而不仅仅是处理单个场景、通过一个路口或完成一次超车。”
“The ultimate goal of autonomous driving is to take passengers safely from any point A to any point B,” Baidu’s Yang said, “rather than just handling an individual scenario, passing through an intersection or completing an overtaking manoeuvre.”