Conventional cameras employed in autonomous vehicle (AV) systems support many
perception tasks, but are challenged by low-light or high dynamic range scenes,
adverse weather, and fast motion. Novel sensors, such as event and thermal
cameras, offer capabilities with the potential to address these scenarios, but
they remain to be fully exploited. This paper introduces the Novel Sensors for
Autonomous Vehicle Perception (NSAVP) dataset to facilitate future research on
this topic. The dataset was captured with a platform including stereo event,
thermal, monochrome, and RGB cameras as well as a high precision navigation
system providing ground truth poses. The data was collected by repeatedly
driving two ~8 km routes and includes varied lighting conditions and opposing
viewpoint perspectives. We provide benchmarking experiments on the task of
place recognition to demonstrate challenges and opportunities for novel sensors
to enhance critical AV perception tasks. To our knowledge, the NSAVP dataset is
the first to include stereo thermal cameras together with stereo event and
monochrome cameras. The dataset and supporting software suite is available at:
this https URL

通过引入 Novel Sensors for Autonomous Vehicle Perception (NSAVP) 数据集，本文旨在促进未来关于此主题的研究，并提供了关于利用新型传感器增强关键自动驾驶感知任务的挑战和机遇的基准实验。

数据集与基准：用于自动驾驶车辆感知的新型传感器

Dataset and Benchmark: Novel Sensors for Autonomous Vehicle Perception

Simulation of autonomous vehicle systems requires that simulated traffic
participants exhibit diverse and realistic behaviors. The use of prerecorded
real-world traffic scenarios in simulation ensures realism but the rarity of
safety critical events makes large scale collection of driving scenarios
expensive. In this paper, we present DJINN - a diffusion based method of
generating traffic scenarios. Our approach jointly diffuses the trajectories of
all agents, conditioned on a flexible set of state observations from the past,
present, or future. On popular trajectory forecasting datasets, we report state
of the art performance on joint trajectory metrics. In addition, we demonstrate
how DJINN flexibly enables direct test-time sampling from a variety of valuable
conditional distributions including goal-based sampling, behavior-class
sampling, and scenario editing.

通过 DJINN 扩散方法，本文在模拟自动驾驶系统中生成交通场景，改善实际情境收集的昂贵性，实现了目标轨迹预测中的最先进效果。同时展示了 DJINN 如何灵活地从各种有价值的条件分布中进行直接测试抽样，包括基于目标、行为类别和场景编辑等。