Perceiving and navigating through work zones is challenging and
under-explored, even with major strides in self-driving research. An important
reason is the lack of open datasets for developing new algorithms to address
this long-tailed scenario. We propose the ROADWork dataset to learn how to
recognize, observe and analyze and drive through work zones. We find that
state-of-the-art foundation models perform poorly on work zones. With our
dataset, we improve upon detecting work zone objects (+26.2 AP), while
discovering work zones with higher precision (+32.5%) at a much higher
discovery rate (12.8 times), significantly improve detecting (+23.9 AP) and
reading (+14.2% 1-NED) work zone signs and describing work zones (+36.7 SPICE).
We also compute drivable paths from work zone navigation videos and show that
it is possible to predict navigational goals and pathways such that 53.6% goals
have angular error (AE) < 0.5 degrees (+9.9 %) and 75.3% pathways have AE < 0.5
degrees (+8.1 %).

提出 ROADWork 数据集，改进了检测工作区对象的性能，发现和描述工作区的能力，并预测可驾驶路径，提高了导航目标和路径的准确性。

ROADWork 数据集：学习识别、观察、分析和驾驶工作区

ROADWork Dataset: Learning to Recognize, Observe, Analyze and Drive  Through Work Zones

Traffic simulation software is used by transportation researchers and
engineers to design and evaluate changes to roadways. These simulators are
driven by models of microscopic driver behavior from which macroscopic measures
like flow and congestion can be derived. Many models are designed for a subset
of possible traffic scenarios and roadway configurations, while others have no
explicit constraints on their application. Work zones (WZs) are one scenario
for which no model to date has reproduced realistic driving behavior. This
makes it difficult to optimize for safety and other metrics when designing a
WZ. The Federal Highway Administration commissioned the USDOT Volpe Center to
develop a car-following (CF) model for use in microscopic simulators that can
capture and reproduce driver behavior accurately within and outside of WZs.
Volpe also performed a naturalistic driving study to collect telematics data
from vehicles driven on roads with WZs for use in model calibration. During
model development, Volpe researchers observed difficulties in calibrating their
model, leaving them to question whether there existed flaws in their model, in
the data, or in the procedure used to calibrate the model using the data. In
this thesis, I use Bayesian methods for data analysis and parameter estimation
to explore and, where possible, address these questions. First, I use Bayesian
inference to measure the sufficiency of the size of the data set. Second, I
compare the procedure and results of the genetic algorithm based calibration
performed by the Volpe researchers with those of Bayesian calibration. Third, I
explore the benefits of modeling CF hierarchically. Finally, I apply what was
learned in the first three phases using an established CF model, Wiedemann 99,
to the probabilistic modeling of the Volpe model. Validation is performed using
information criteria as an estimate of predictive accuracy.

使用贝叶斯方法进行数据分析和参数估计，评估交通仿真软件中基于遗传算法的校准过程，研究车辆跟驰模型在模型校准中的层次化建模优势，并应用概率建模对车辆跟驰模型进行验证。