A profound understanding of inter-agent relationships and motion behaviors is
important to achieve high-quality planning when navigating in complex
scenarios, especially at urban traffic intersections. We present a trajectory
prediction approach with respect to traffic lights, D2-TPred, which uses a
spatial dynamic interaction graph (SDG) and a behavior dependency graph (BDG)
to handle the problem of discontinuous dependency in the spatial-temporal
space. Specifically, the SDG is used to capture spatial interactions by
reconstructing sub-graphs for different agents with dynamic and changeable
characteristics during each frame. The BDG is used to infer motion tendency by
modeling the implicit dependency of the current state on priors behaviors,
especially the discontinuous motions corresponding to acceleration,
deceleration, or turning direction. Moreover, we present a new dataset for
vehicle trajectory prediction under traffic lights called VTP-TL. Our
experimental results show that our model achieves more than {20.45% and 20.78%
}improvement in terms of ADE and FDE, respectively, on VTP-TL as compared to
other trajectory prediction algorithms. The dataset and code are available at:
this https URL

通过使用空间动态相互作用图和行为依赖图来处理空间 - 时间空间中的依赖性问题，我们提出了一种与交通灯相对应的轨迹预测方法 D2-TPred。通过车辆轨迹预测数据集 VTP-TL 的实验结果证明，我们的模型相比其他轨迹预测算法在 ADE 和 FDE 方面分别提高了 20.45％和 20.78％。

D2-TPred: 交通信号灯下轨迹预测的间断依赖

D2-TPred: Discontinuous Dependency for Trajectory Prediction under  Traffic Lights

Autonomous driving systems need to handle complex scenarios such as lane
following, avoiding collisions, taking turns, and responding to traffic
signals. In recent years, approaches based on end-to-end behavioral cloning
have demonstrated remarkable performance in point-to-point navigational
scenarios, using a realistic simulator and standard benchmarks. Offline
imitation learning is readily available, as it does not require expensive hand
annotation or interaction with the target environment, but it is difficult to
obtain a reliable system. In addition, existing methods have not specifically
addressed the learning of reaction for traffic lights, which are a rare
occurrence in the training datasets. Inspired by the previous work on
multi-task learning and attention modeling, we propose a novel multi-task
attention-aware network in the conditional imitation learning (CIL) framework.
This does not only improve the success rate of standard benchmarks, but also
the ability to react to traffic lights, which we show with standard benchmarks.

本文提出了一种基于多任务学习和注意力模型的新型自动驾驶条件模仿学习网络，通过解决交通灯反应问题，不仅提高了标准基准测试的成功率，而且具备了响应交通信号的能力。

面向端到端自动驾驶的多任务学习与注意力机制

Multi-task Learning with Attention for End-to-end Autonomous Driving

Most existing approaches to autonomous driving fall into one of two
categories: modular pipelines, that build an extensive model of the
environment, and imitation learning approaches, that map images directly to
control outputs. A recently proposed third paradigm, direct perception, aims to
combine the advantages of both by using a neural network to learn appropriate
low-dimensional intermediate representations. However, existing direct
perception approaches are restricted to simple highway situations, lacking the
ability to navigate intersections, stop at traffic lights or respect speed
limits. In this work, we propose a direct perception approach which maps video
input to intermediate representations suitable for autonomous navigation in
complex urban environments given high-level directional inputs. Compared to
state-of-the-art reinforcement and conditional imitation learning approaches,
we achieve an improvement of up to 68 % in goal-directed navigation on the
challenging CARLA simulation benchmark. In addition, our approach is the first
to handle traffic lights and speed signs by using image-level labels only, as
well as smooth car-following, resulting in a significant reduction of traffic
accidents in simulation.

该研究提出了一种基于直接感知的自主驾驶方法，在城市环境中能够根据高级方向输入进行自主导航，同时能够识别交通信号灯和速度标志，降低车辆事故率。