Multi-modal Transformer Path Prediction for autonomous vehicle

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题名:	Multi-modal Transformer Path Prediction for autonomous vehicle
作者:	曾嘉鴻;Tseng, Chia-Hong
贡献者:	資訊工程學系
关键词:	自動駕駛;Autonomous vehicle;Path Prediction;Deep Learning
日期:	2022-07-21
上传时间:	2022-10-04 11:59:31 (UTC+8)
出版者:	國立中央大學
摘要:	車輛軌跡預測在自動駕駛系統中是一個具有挑戰性的題目，且攸關自駕車行駛在道路上的安危。近年來有很多的研究者都在研究這個題目，然而很多研究並沒有使用到道路資訊和Transformer的架構。藉由自駕車上面不同的感知器所收集到的資料，我們提出一套軌跡預測系統用來預測車輛接下來的行徑軌跡。為了達到更精準的預測，我們的模型採用修改過的transformer架構。我們為了更好的運用道路資訊，我們在資料預處理的時候會將一些與車輛行徑方向不同的道路刪除，除此之外我們也將一些較小的道路結合將道路資料處理到符合模型的輸入。在最後我們用nuScene資料集做了很多實驗來驗證我們所提出的系統是有效的。 ;Reasoning about vehicle path prediction is an essential and challanging problem for the safe operation of autonomous driving systems. There existing many research works for path prediction. However, most of them do not use lane information and are not based on the Transformer architecture. By utilizing different types of data collected from sensors equipped on the self-driving vehicles, we propose a path prediction system named Multi-model Transformer Path Prediction (MTPP) that aims to predict long-term future trajectory of target agents. To achieve more accurate path prediction, the Transformer architecture is adopted in our model. To better utilize the lane information, the lanes which are in opposite direction to target agent are not likely to be taken by the target agent and are consequently filtered out. In addition, consecutive lane chunks are combined to ensure the lane input to be long enough for path prediction. An extensive evaluation is conducted to show the efficacy of the proposed system using nuScene, a real-world trajectory forecasting dataset.
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