In automated driving, highly accurate maps are commonly used to support and
complement perception. These maps are costly to create and quickly become
outdated as the traffic world is permanently changing. In order to support or
replace the map of an automated system with detections from sensor data, a
perception module must be able to detect the map features. We propose a neural
network that follows the one shot philosophy of YOLO but is designed for
detection of 1D structures in images, such as lane boundaries.
We extend previous ideas by a midpoint based line representation and anchor
definitions. This representation can be used to describe lane borders,
markings, but also implicit features such as centerlines of lanes. The broad
applicability of the approach is shown with the detection performance on lane
centerlines, lane borders as well as the markings both on highways and in urban
areas.
Versatile lane boundaries are detected and can be inherently classified as
dashed or solid lines, curb, road boundaries, or implicit delimitation.

基于神经网络，通过一次性检测的方法，研究在自动驾驶中以图像为基础的 1D 结构检测，如车道边界。提出了一种基于中点和锚点定义的车道边界表示方法，能够检测车道中心线、车道边界以及高速公路和城市区域中的标志，实现了多功能检测和分类。

YOLinO++：用于无地图自动潜水的多功能折线单次估计

YOLinO++: Single-Shot Estimation of Generic Polylines for Mapless  Automated Diving

This paper addresses the advancements in on-road trajectory planning for
Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a
globally optimal route for APVs, considering various factors such as vehicle
dynamics, constraints, and detected obstacles. Traditional techniques involve a
combination of sampling methods followed by optimization algorithms, where the
former ensures global awareness and the latter refines for local optima.
Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR)
optimization algorithm has recently emerged, adapted for APV systems,
emphasizing improved safety and comfort. However, existing implementations
utilizing the vehicle bicycle kinematic model may not guarantee controllable
trajectories. We augment this model by incorporating higher-order terms,
including the first and second-order derivatives of curvature and longitudinal
jerk. This inclusion facilitates a richer representation in our cost and
constraint design. We also address roadway compliance, emphasizing adherence to
lane boundaries and directions, which past work often overlooked. Lastly, we
adopt a relaxed logarithmic barrier function to address the CILQR's dependency
on feasible initial trajectories. The proposed methodology is then validated
through simulation and real-world experiment driving scenes in real time.

这篇论文研究了自动乘客车辆的在路轨迹规划，包括采样方法和优化算法，特别关注车辆的安全和舒适性以及车道边界的遵循。

将高阶动力学和道路响应整合到有约束的基于 ILQR 的自主车辆轨迹规划中

Integrating Higher-Order Dynamics and Roadway-Compliance into  Constrained ILQR-based Trajectory Planning for Autonomous Vehicles

One of the fundamental challenges to scale self-driving is being able to
create accurate high definition maps (HD maps) with low cost. Current attempts
to automate this process typically focus on simple scenarios, estimate
independent maps per frame or do not have the level of precision required by
modern self driving vehicles. In contrast, in this paper we focus on drawing
the lane boundaries of complex highways with many lanes that contain topology
changes due to forks and merges. Towards this goal, we formulate the problem as
inference in a directed acyclic graphical model (DAG), where the nodes of the
graph encode geometric and topological properties of the local regions of the
lane boundaries. Since we do not know a priori the topology of the lanes, we
also infer the DAG topology (i.e., nodes and edges) for each region. We
demonstrate the effectiveness of our approach on two major North American
Highways in two different states and show high precision and recall as well as
89% correct topology.

本文通过使用有向无环图模型 (DAG) 来推断车道边界的几何和拓扑属性，从而提高制作高精度地图的效率。经测试，该方法在两个不同的州的两条北美高速公路上表现出了 89% 的正确率。