標題: 「自動駕驶」訓練集優化之扩充数据量真的可以提高訓練精准度嗎? [打印本頁] 作者: admin 時間: 2024-4-24 18:02 標題: 「自動駕驶」訓練集優化之扩充数据量真的可以提高訓練精准度嗎? Advanced driver-assistance systems (ADAS) are nowadays not only a standard accessory in new cars, but also an important milestone on the road to autonomous dri新店汽車借款,ving.
高档駕驶辅助體系（ADAS）现在不但是新車的標配，也是主動駕驶門路上的一個首要里程碑。
Be it keeping the lane and distance to the vehicle in front or parking in tight spaces: The more the technical assistants are supposed to be able to do independently, the better the neural networks on which the systems are based have to be trained for this. Accordingly, the data sets used continue to grow.
Yet, this raises a question: to what extent do the training data actually reflec能量梳，t the operational domains of the ADAS? This is often of secondary importance and rarely checked. In order to reduce the systems’ susceptibility to errors, only the quantity of data has been kept constantly increasing up to now. This results in unnecessarily complex, lengthy and thus also inefficient development processes.
ARRK Engineering has therefore developed an approach to analyze the models with regard to concrete operational domains and relevant scenarios, such as urban traffic or motorways. Data that is inaccurate or distorts reality can be corrected or removed and the ADAS can be trained in a targeted, reliable and at the same time resource-efficient manner.
Well-known OEMs are leading the way, mobility start-ups are following suit and consumers want it: more and more vehicles are being equipped with level 2 and level 3 driver-assistance systems.
Thus, every day, numerous road users rely on lane keeping assists or autosteer (LKA/LCA), automated parking and adaptive cruise control (ACC). The general safety on the roads – and thus the safety of all ro支票借錢,ad users – therefore depends to a large extent on the proper functioning of these systems. To ensure this, their neuronal networks are trained with the help of huge data sets. The models are intended to represent all possible situations that the vehicle may encounter in everyday traffic and thus serve as a recognition and calculation basis for the autonomous reactions of the ADAS in the field.
The more complex the functionalities of different ADAS, the more specific data sets are required for their training. In order to cover all possible traffic situations, the data sets have been expanded more and more in recent years, focusing primarily on mass, i.e. the sheer number of recording hours or of annotated objects in different weather and lighting conditions.
However, this inevitably increases the proportion of data that is inaccurate or simply unsuitable for a particular operational domain. To ensure that the newly developed ADAS continue to function reliably, their quality deficit has in turn been compensated for with quantity – a vicious cycle. This has already led to very long development times with many iteration loops, in which the training of the neural networks alone takes several weeks.
To escape this dile妹妹a, the automotive industry needs to shift the focus away from quantity and towards quality of data sets. Therefore, the machine learning specialists at ARRK Engineering have developed an approach to validate the processes with regard to an operational domain and to correct them if necessary. In this way, development can be made more efficient and, more importantly, the functional safety of the ADAS can be increased – an essential prerequisite for its future further development into higher levels of autonomous driving.
The above content was created by Václav Diviš, Senior Expert Machine Learning at ARRK Engineering, and presented at the SafetyAI 2023 conference. In the next issue, we'll continue to share the case studies from the report and provide a way to download the full report, so stay tuned!