论文标题
trajformer：自动驾驶的局部自动诉讼环境的轨迹预测
Trajformer: Trajectory Prediction with Local Self-Attentive Contexts for Autonomous Driving
论文作者
论文摘要
有效的功能萃取对于模型的上下文理解至关重要，特别是对于机器人和自主驾驶的应用，例如多模式轨迹预测。但是，最新的生成方法在表示场景上下文时面临限制，从而预测了不可接受的未来。我们通过使用自我注意来缓解这些局限性，从而可以更好地控制代表代理人的社会环境；我们提出了一条局部特征抽取管道，该管道可在下游产生更多显着信息，并提高参数效率。我们显示了对Argoverse数据集上各个基线的标准指标（Minade，Minfde，Dao，DAC）的改进。我们在以下网址发布代码：https：//github.com/manojbhat09/trajformer
Effective feature-extraction is critical to models' contextual understanding, particularly for applications to robotics and autonomous driving, such as multimodal trajectory prediction. However, state-of-the-art generative methods face limitations in representing the scene context, leading to predictions of inadmissible futures. We alleviate these limitations through the use of self-attention, which enables better control over representing the agent's social context; we propose a local feature-extraction pipeline that produces more salient information downstream, with improved parameter efficiency. We show improvements on standard metrics (minADE, minFDE, DAO, DAC) over various baselines on the Argoverse dataset. We release our code at: https://github.com/Manojbhat09/Trajformer
