Accurate motion prediction of pedestrians, cyclists, and other surrounding
vehicles (all called agents) is very important for autonomous driving. Most
existing works capture map information through an one-stage interaction with
map by vector-based attention, to provide map constraints for social
interaction and multi-modal differentiation. However, these methods have to
encode all required map rules into the focal agent's feature, so as to retain
all possible intentions' paths while at the meantime to adapt to potential
social interaction. In this work, a progressive interaction network is proposed
to enable the agent's feature to progressively focus on relevant maps, in order
to better learn agents' feature representation capturing the relevant map
constraints. The network progressively encode the complex influence of map
constraints into the agent's feature through graph convolutions at the
following three stages: after historical trajectory encoder, after social
interaction, and after multi-modal differentiation. In addition, a weight
allocation mechanism is proposed for multi-modal training, so that each mode
can obtain learning opportunities from a single-mode ground truth. Experiments
have validated the superiority of progressive interactions to the existing
one-stage interaction, and demonstrate the effectiveness of each component.
Encouraging results were obtained in the challenging benchmarks.

通过逐步交互网络和权重分配机制提高地图约束条件对代理的特征表示的学习，从而实现准确的行为预测，以优化自动驾驶性能。

基于递进交互的自动驾驶轨迹预测学习

ProIn: Learning to Predict Trajectory Based on Progressive Interactions  for Autonomous Driving

Predicting the future behavior of agents is a fundamental task in autonomous
vehicle domains. Accurate prediction relies on comprehending the surrounding
map, which significantly regularizes agent behaviors. However, existing methods
have limitations in exploiting the map and exhibit a strong dependence on
historical trajectories, which yield unsatisfactory prediction performance and
robustness. Additionally, their heavy network architectures impede real-time
applications. To tackle these problems, we propose Map-Agent Coupled
Transformer (MacFormer) for real-time and robust trajectory prediction. Our
framework explicitly incorporates map constraints into the network via two
carefully designed modules named coupled map and reference extractor. A novel
multi-task optimization strategy (MTOS) is presented to enhance learning of
topology and rule constraints. We also devise bilateral query scheme in context
fusion for a more efficient and lightweight network. We evaluated our approach
on Argoverse 1, Argoverse 2, and nuScenes real-world benchmarks, where it all
achieved state-of-the-art performance with the lowest inference latency and
smallest model size. Experiments also demonstrate that our framework is
resilient to imperfect tracklet inputs. Furthermore, we show that by combining
with our proposed strategies, classical models outperform their baselines,
further validating the versatility of our framework.

利用地图约束的实时和鲁棒的轨迹预测方法，通过两个为地图和参考提取器设计的模块显式地将地图约束纳入网络中，并通过新的多任务优化策略来提高拓扑和规则约束的学习。通过在上下文融合中采用双边查询方案来实现更高效和轻量级的网络。实验证明，我们的方法在现实世界的基准测试中取得了最先进的性能，具有最低的推理延迟和最小的模型尺寸，并且对不完美的轨迹输入具有韧性。此外，我们还展示了通过与我们提出的策略相结合，经典模型优于其基准模型，进一步验证了我们的框架的多功能性。