Precisely predicting the future trajectories of surrounding traffic
participants is a crucial but challenging problem in autonomous driving, due to
complex interactions between traffic agents, map context and traffic rules.
Vector-based approaches have recently shown to achieve among the best
performances on trajectory prediction benchmarks. These methods model simple
interactions between traffic agents but don't distinguish between relation-type
and attributes like their distance along the road. Furthermore, they represent
lanes only by sequences of vectors representing center lines and ignore context
information like lane dividers and other road elements. We present a novel
approach for vector-based trajectory prediction that addresses these
shortcomings by leveraging three crucial sources of information: First, we
model interactions between traffic agents by a semantic scene graph, that
accounts for the nature and important features of their relation. Second, we
extract agent-centric image-based map features to model the local map context.
Finally, we generate anchor paths to enforce the policy in multi-modal
prediction to permitted trajectories only. Each of these three enhancements
shows advantages over the baseline model HoliGraph.

利用语义场景图、基于图像的地图特征以及锚路径生成，我们提出了一种改进的基于向量的轨迹预测方法，解决了交通参与者之间的复杂交互、地图背景和交通规则之间的挑战性问题，相较于基准模型 HoliGraph，我们的方法显示出明显的优势。

基于异构图的轨迹预测：局部地图环境和社交互动

Heterogeneous Graph-based Trajectory Prediction using Local Map Context  and Social Interactions

Pseudo Relevance Feedback (PRF) is known to improve the effectiveness of
bag-of-words retrievers. At the same time, deep language models have been shown
to outperform traditional bag-of-words rerankers. However, it is unclear how to
integrate PRF directly with emergent deep language models. In this article, we
address this gap by investigating methods for integrating PRF signals into
rerankers and dense retrievers based on deep language models. We consider
text-based and vector-based PRF approaches, and investigate different ways of
combining and scoring relevance signals. An extensive empirical evaluation was
conducted across four different datasets and two task settings (retrieval and
ranking). Text-based PRF results show that the use of PRF had a mixed effect on
deep rerankers across different datasets. We found that the best effectiveness
was achieved when (i) directly concatenating each PRF passage with the query,
searching with the new set of queries, and then aggregating the scores; (ii)
using Borda to aggregate scores from PRF runs. Vector-based PRF results show
that the use of PRF enhanced the effectiveness of deep rerankers and dense
retrievers over several evaluation metrics. We found that higher effectiveness
was achieved when (i) the query retains either the majority or the same weight
within the PRF mechanism, and (ii) a shallower PRF signal (i.e., a smaller
number of top-ranked passages) was employed, rather than a deeper signal. Our
vector-based PRF method is computationally efficient; thus this represents a
general PRF method others can use with deep rerankers and dense retrievers.

本文研究了将 Pseudo Relevance Feedback 方法集成到基于深度语言模型的 rerankers 和 dense retrievers 中的方法，并对基于文本和向量的 PRF 方法进行了考虑和评估。在四个数据集和两个任务设置下进行了广泛的实证评估。结果表明，vector-based PRF 方法有效提高了 deep rerankers 和 dense retrievers 的检索和排名效果。