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- W4380451158 abstract "The probability of a pedestrian being hit by a self-driving car is very high because of the varying attributes of pedestrians on the road. In the proposed work an end-to-end framework has been developed which identifies five different parameters which are essential in situations when a pedestrian comes in front of the vehicle. The end-to-end framework has been created using multitask learning and common data-sets to obtain a single network to provide faster and accurate results which helps to make decisions in real time. The time taken to obtain the result is much faster when compared to the existing system. The results obtained are much better than the existing system. All the modules were analyzed and their advantages, limitations, use-case are explained in the proposed work." @default.
- W4380451158 created "2023-06-14" @default.
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- W4380451158 date "2023-01-01" @default.
- W4380451158 modified "2023-10-14" @default.
- W4380451158 title "End to End Pedestrian Features Detection for Self Driving Cars" @default.
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- W4380451158 doi "https://doi.org/10.1007/978-981-99-0769-4_52" @default.
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