Conditional Imitation learning is a common and effective approach to train
autonomous driving agents. However, two issues limit the full potential of this
approach: (i) the inertia problem, a special case of causal confusion where the
agent mistakenly correlates low speed with no acceleration, and (ii) low
correlation between offline and online performance due to the accumulation of
small errors that brings the agent in a previously unseen state. Both issues
are critical for state-aware models, yet informing the driving agent of its
internal state as well as the state of the environment is of crucial
importance. In this paper we propose a multi-task learning agent based on a
multi-stage vision transformer with state token propagation. We feed the state
of the vehicle along with the representation of the environment as a special
token of the transformer and propagate it throughout the network. This allows
us to tackle the aforementioned issues from different angles: guiding the
driving policy with learned stop/go information, performing data augmentation
directly on the state of the vehicle and visually explaining the model's
decisions. We report a drastic decrease in inertia and a high correlation
between offline and online metrics.

本文提出了一种基于多阶段视觉 Transformer 的多任务学习代理，通过传播车辆状态和环境表示作为 Transformer 的特殊令牌，并从不同角度解决了惯性和离线与在线性能之间的低相关性问题。在实验中，我们观察到惯性显著减少，并且离线和在线指标之间有很高的相关性。

解决自动驾驶中状态感知模仿学习的局限性

Addressing Limitations of State-Aware Imitation Learning for Autonomous  Driving

Agents operating in physical environments need to be able to handle delays in
the input and output signals since neither data transmission nor sensing or
actuating the environment are instantaneous. Shields are
correct-by-construction runtime enforcers that guarantee safe execution by
correcting any action that may cause a violation of a formal safety
specification. Besides providing safety guarantees, shields should interfere
minimally with the agent. Therefore, shields should pick the safe corrective
actions in such a way that future interferences are most likely minimized.
Current shielding approaches do not consider possible delays in the input
signals in their safety analyses. In this paper, we address this issue. We
propose synthesis algorithms to compute \emph{delay-resilient shields} that
guarantee safety under worst-case assumptions on the delays of the input
signals. We also introduce novel heuristics for deciding between multiple
corrective actions, designed to minimize future shield interferences caused by
delays. As a further contribution, we present the first integration of shields
in a realistic driving simulator. We implemented our delayed shields in the
driving simulator \textsc{Carla}. We shield potentially unsafe autonomous
driving agents in different safety-critical scenarios and show the effect of
delays on the safety analysis.

该论文提出针对物理环境中智能体输入和输出信号延迟问题的合成算法，用于计算延迟鲁棒盾牌，以保证机器人在自动驾驶的环境下的安全执行，并在实际驾驶模拟器中验证实现效果。

延迟观测下的安全屏障

Safety Shielding under Delayed Observation

In the real world, autonomous driving agents navigate in highly dynamic
environments full of unexpected situations where pre-trained models are
unreliable. In these situations, what is immediately available to vehicles is
often only human operators. Empowering autonomous driving agents with the
ability to navigate in a continuous and dynamic environment and to communicate
with humans through sensorimotor-grounded dialogue becomes critical. To this
end, we introduce Dialogue On the ROad To Handle Irregular Events (DOROTHIE), a
novel interactive simulation platform that enables the creation of unexpected
situations on the fly to support empirical studies on situated communication
with autonomous driving agents. Based on this platform, we created the Situated
Dialogue Navigation (SDN), a navigation benchmark of 183 trials with a total of
8415 utterances, around 18.7 hours of control streams, and 2.9 hours of trimmed
audio. SDN is developed to evaluate the agent's ability to predict dialogue
moves from humans as well as generate its own dialogue moves and physical
navigation actions. We further developed a transformer-based baseline model for
these SDN tasks. Our empirical results indicate that language guided-navigation
in a highly dynamic environment is an extremely difficult task for end-to-end
models. These results will provide insight towards future work on robust
autonomous driving agents. The DOROTHIE platform, SDN benchmark, and code for
the baseline model are available at this https URL.

介绍了一种基于 DOROTHIE 平台的 Situated Dialogue Navigation (SDN) 导航基准，考察自主驾驶代理通过对话进行动态环境下导航的能力，结果表明，端到端模型在这种高度动态的语言引导导航任务中是非常困难的。