人工城市可以为无人驾驶领养铺平道路

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互联和自主车辆有未来

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这是毫无疑问的,但仍然需要确保它们我们的高速公路上是安全的,并减轻公众对安全的担忧,以便未来几年内提高它们的采用率。CAV需要能够对不可预见的事件做出反应——就像我们作为人类驾驶员所做的那样

互联和自主车辆(CAV)有未来

这是毫无疑问的,但仍然需要确保它们我们的高速公路上是安全的,并减轻公众对安全的担忧,以便未来几年内提高它们的采用率。CAV需要能够对不可预见的事件做出反应——就像我们作为人类驾驶员所做的那样

驾驶员面临的任何障碍都是无法预料的,因此这些车辆的人工智能和机器学习需要能够做出相应的反应,以防止事故发生,无论天气、环境如何,不管他们什么样的环境下经营,无论是农村还是城市

边缘案例场景

然而,Nvidia汽车高级总监丹尼·夏皮罗(Danny Shapiro)认为,“正常情况下的自动驾驶是一个已解决的问题。”他说,边缘案例是导致自动驾驶困难的原因,同时他还提到“罕见和困难的情况,往往包括各种挑战。”这可能是,例如,有人不小心失去了对滚上高速公路的购物车的控制。这不是一个常见的事件,但它可能发生

他补充道:“还有一个更复杂的问题,那就是确保汽车能够应对复杂的情况,比如夜间、雨中发生这种情况,还有一个额外的司机你前面挡住了大车的视线,直到最后一分钟。”

艾丽丝·索尔特她的文章《人工城市是AV道路安全的答案吗?:“虽然您可以连续驾驶几天而不会遇到道路上最常见的障碍物,但人工试验城市等受控环境中,技术人员可以保证这些障碍物的存。事实证明,这对于形成公众对这项技术的看法和信任至关重要。虽然你可以连续驾驶几天,而不会遇到道路上最常见的障碍,但人工试验城市等受控环境中,技术人员可以保证这些障碍。”

夏皮罗回应道:“为了确保一辆车真正的安全,它需要测试这些情况,以及各种各样的排列,反复作为开发和验证的一部分。现实世界中,这种情况很少道路测试中遇到,而且肯定不会以完全相同的方式发生两次。因此,我们需要一个替代方案,以确保汽车知道如何正确应对。”

“人造城市中的模拟(反映现实)提供了一种反复测试相同情况的方法。它还允许我们以可控的方式添加变化。这意味着我们可以帮助加快进度,因为我们花时间测试困难的事情,而且,由于它是可重复的,我们可以工作中衡量进度。”

最大化连接性

Stéphane Barbier是Transplis SAS的首席业务发展官,该公司自称是“欧洲唯一致力于创新交通系统和道路设备的智能城市实验室”。他解释说,将人工城市用于CAV开发的一种方案是,对其设计和建设进行优化,以“最大限度地连接设备和智能基础设施,以便为CAV提供最佳连接。”

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英文译文:

Connected and autonomous vehicles (CAVs) have a future.

That is without doubt but there is still a need to ensure that they will be safe on our highways and to ease the public’s safety concerns to increase their adoption over the next few years. CAVs need to be able to react to unforeseen events – just as we do as human drivers.

None of the obstacles that drivers face can be anticipated and so the artificial intelligence and machine learning of these vehicles needs to be able to react accordingly to prevent accidents from occurring, no matter the weather, no matter the circumstances, and not matter what kind of environment – both rural urban – they are to operate in.

Edge-case scenarios

Danny Shapiro, senior director automotive at Nvidia, nevertheless believes that “autonomous driving in normal situations is a solved problem.”  Edge cases are what makes autonomous driving hard, he says while referring to the “rare and difficult situations that often include a combination of challenges.” This might be, for example, someone accidentally losing control of a shopping trolley that rolls onto the highway. It’s not a common event, but it could happen.

He adds: “Then there’s the added complexity of making sure the car can address compounding conditions, such as having this occur at night, in the rain, and with an additional driver in front of you who blocks the view of the cart until the last minute.”

Alice Salter writes for 2025AD in her article, “While you could drive for days without encountering some of even the most common obstacles you’ll find on the road, in controlled environments like artificial test cities, technicians can guarantee them. This proves vital in forming public opinion of, and trust in, the tech. While you could drive for days without encountering some of even the most common obstacles you’ll find on the road, in controlled environments like artificial test cities, technicians can guarantee them.”

Shapiro responds: “In order to ensure a car is truly safe, it requires testing these situations, and a wide range of permutations, repeatedly as part of development and validation.  In the real world, this situation is rarely encountered in on-road testing and certainly never happens exactly the same way twice. Therefore, we need an alternative to ensure the car knows how to properly respond.”

“Simulation in an artificial city (that mirrors reality) provides a way to test the same situations over and over.  It also allows us to add variations in a controlled way.  This means we can help speed progress since we spend time testing the hard things and, because it’s repeatable, we can measure progress as we work.”

Maximising connectivity

Stéphane Barbier is chief business development officer at Transpolis SAS, which describes itself as being “the unique smart city lab in Europe dedicated to innovative transportation systems and road equipment”. He explains that one scenario for the use of artificial cities for CAV development could see their design and construction being optimised to have “maximum of connected devices and intelligent infrastructures, in order to have the best connectivity for CAV.”

 

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