상세정보
학술대회
Hierarchical Latent Structure for Multi-Modal Vehicle Trajectory Forecasting
- 발행일
- 202210
- 출처
- European Conference on Computer Vision (ECCV) 2022 (LNCS 13682), pp.129-145
- 협약과제
- 22HS4600, 대중교통 소외지역 이동 및 생활안전 사회문제해결을 위한 표준플랫폼 기반 자율주행기술개발, 민경욱
- 초록
- Variational autoencoder (VAE) has widely been utilized for modeling data distributions because it is theoretically elegant, easy to train, and has nice manifold representations. However, when applied to image reconstruction and synthesis tasks, VAE shows the limitation that the generated sample tends to be blurry. We observe that a similar problem, in which the generated trajectory is located between adjacent lanes, often arises in VAE-based trajectory forecasting models. To mitigate this problem, we introduce a hierarchical latent structure into the VAE-based forecasting model. Based on the assumption that the trajectory distribution can be approximated as a mixture of simple distributions (or modes), the low-level latent variable is employed to model each mode of the mixture and the high-level latent variable is employed to represent the weights for the modes. To model each mode accurately, we condition the low-level latent variable using two lane-level context vectors computed in novel ways, one corresponds to vehicle-lane interaction and the other to vehicle-vehicle interaction. The context vectors are also used to model the weights via the proposed mode selection network. To evaluate our forecasting model, we use two large-scale real-world datasets. Experimental results show that our model is not only capable of generating clear multi-modal trajectory distributions but also outperforms the state-of-the-art (SOTA) models in terms of prediction accuracy. Our code is available at https://github.com/d1024choi/HLSTrajForecast.
- KSP 제안 키워드
- Context vector, Data Distribution, Image reconstruction, Latent Variable, Mode Selection, Multi-modal trajectory, Prediction accuracy, Real-world, Trajectory forecasting, Vehicle interaction, Vehicle trajectory