In the past two decades, autonomous driving has been catalyzed into reality
by the growing capabilities of machine learning. This paradigm shift possesses
significant potential to transform the future of mobility and reshape our
society as a whole. With the recent advances in perception, planning, and
control capabilities, autonomous driving technologies are being rolled out for
public trials, yet we remain far from being able to rigorously ensure the
resilient operations of these systems across the long-tailed nature of the
driving environment. Given the limitations of real-world testing, autonomous
vehicle simulation stands as the critical component in exploring the edge of
autonomous driving capabilities, developing the robust behaviors required for
successful real-world operation, and enabling the extraction of hidden risks
from these complex systems prior to deployment. This paper presents the current
state-of-the-art simulation frameworks and methodologies used in the
development of autonomous driving systems, with a focus on outlining how
simulation is used to build the resiliency required for real-world operation
and the methods developed to bridge the gap between simulation and reality. A
synthesis of the key challenges surrounding autonomous driving simulation is
presented, specifically highlighting the opportunities to further advance the
ability to continuously learn in simulation and effectively transfer the
learning into the real-world - enabling autonomous vehicles to exit the
guardrails of simulation and deliver robust and resilient operations at scale.

利用模拟系统开发自动驾驶技术，以实现真实世界操作中的鲁棒性。

退出仿真：实现大规模强健和韧性自主车辆的道路

Exiting the Simulation: The Road to Robust and Resilient Autonomous Vehicles at Scale

Although pre-trained sequence-to-sequence models have achieved great success
in dialogue response generation, chatbots still suffer from generating
inconsistent responses in real-world practice, especially in multi-turn
settings. We argue that this can be caused by a discrepancy between training
and real-world testing. At training time, chatbot generates the response with
the golden context, while it has to generate based on the context consisting of
both user utterances and the model predicted utterances during real-world
testing. With the growth of the number of utterances, this discrepancy becomes
more serious in the multi-turn settings. In this paper, we propose a
hierarchical sampling-based method consisting of both utterance-level sampling
and semi-utterance-level sampling, to alleviate the discrepancy, which
implicitly increases the dialogue coherence. We further adopt reinforcement
learning and re-ranking methods to explicitly optimize the dialogue coherence
during training and inference, respectively. Empirical experiments show the
effectiveness of the proposed methods for improving the robustness of chatbots
in real practice.

通过采用分层抽样的方法，引入了强化学习和重新排序机制，以提高聊天机器人在实际应用场景中的鲁棒性，并缓解聊天机器人在多轮对话中生成不一致响应的问题。

面向稳健的在线对话响应生成

Towards Robust Online Dialogue Response Generation

How can an end-user provide feedback if a deployed structured prediction
model generates inconsistent output, ignoring the structural complexity of
human language? This is an emerging topic with recent progress in synthetic or
constrained settings, and the next big leap would require testing and tuning
models in real-world settings. We present a new dataset, Interscript,
containing user feedback on a deployed model that generates complex everyday
tasks. Interscript contains 8,466 data points -- the input is a possibly
erroneous script and a user feedback, and the output is a modified script. We
posit two use-cases of \ours that might significantly advance the
state-of-the-art in interactive learning. The dataset is available at:
this https URL

本研究提出了一个名为 Interscript 的数据集，其中包含用户反馈的部署模型的数据，可以用于测试和调整在实际环境中使用的模型的性能，这可以在交互式学习中显着提高科技水平。