Global Positioning System (GPS) navigation provides accurate positioning with
global coverage, making it a reliable option in open areas with unobstructed
sky views. However, signal degradation may occur in indoor spaces and urban
canyons. In contrast, Inertial Measurement Units (IMUs) consist of gyroscopes
and accelerometers that offer relative motion information such as acceleration
and rotational changes. Unlike GPS, IMUs do not rely on external signals,
making them useful in GPS-denied environments. Nonetheless, IMUs suffer from
drift over time due to the accumulation of errors while integrating
acceleration to determine velocity and position. Therefore, fusing the GPS and
IMU is crucial for enhancing the reliability and precision of navigation
systems in autonomous vehicles, especially in environments where GPS signals
are compromised. To ensure smooth navigation and overcome the limitations of
each sensor, the proposed method fuses GPS and IMU data. This sensor fusion
uses the Unscented Kalman Filter (UKF) Bayesian filtering technique. The
proposed navigation system is designed to be robust, delivering continuous and
accurate positioning critical for the safe operation of autonomous vehicles,
particularly in GPS-denied environments. This project uses KITTI GNSS and IMU
datasets for experimental validation, showing that the GNSS-IMU fusion
technique reduces GNSS-only data's RMSE. The RMSE decreased from 13.214,
13.284, and 13.363 to 4.271, 5.275, and 0.224 for the x-axis, y-axis, and
z-axis, respectively. The experimental result using UKF shows promising
direction in improving autonomous vehicle navigation using GPS and IMU sensor
fusion using the best of two sensors in GPS-denied environments.

GPS 和 IMU 传感器融合在 GPS 无信号环境下提升自主车辆导航的可靠性和精度的方法。

可靠自动驾驶车辆位置估计的 GPS-IMU 传感器融合

GPS-IMU Sensor Fusion for Reliable Autonomous Vehicle Position  Estimation

Semantic scene completion (SSC) has recently gained popularity because it can
provide both semantic and geometric information that can be used directly for
autonomous vehicle navigation. However, there are still challenges to overcome.
SSC is often hampered by occlusion and short-range perception due to sensor
limitations, which can pose safety risks. This paper proposes a fundamental
solution to this problem by leveraging vehicle-to-vehicle (V2V) communication.
We propose the first generalized collaborative SSC framework that allows
autonomous vehicles to share sensing information from different sensor views to
jointly perform SSC tasks. To validate the proposed framework, we further build
V2VSSC, the first V2V SSC benchmark, on top of the large-scale V2V perception
dataset OPV2V. Extensive experiments demonstrate that by leveraging V2V
communication, the SSC performance can be increased by 8.3% on geometric metric
IoU and 6.0% mIOU.

通过车到车通信，提出了一种解决语义场景补全 (SSC) 中的遮挡和短距离感知等问题的新框架，实验证明通过车到车通信可以提高 8.3％的几何度量 IoU 和 6.0％的平均 IoU (SSC 性能)。

V2VSSC：一项用于感知的三维语义场景补完基准测试，基于车辆到车辆通信

V2VSSC: A 3D Semantic Scene Completion Benchmark for Perception with  Vehicle to Vehicle Communication

Uncertainty in control and perception poses challenges for autonomous vehicle
navigation in unstructured environments, leading to navigation failures and
potential vehicle damage. This paper introduces a framework that minimizes
control and perception uncertainty to ensure safe and reliable navigation. The
framework consists of two uncertainty-aware models: a learning-based vehicle
dynamics model and a self-supervised traversability estimation model. We train
a vehicle dynamics model that can quantify the epistemic uncertainty of the
model to perform active exploration, resulting in the efficient collection of
training data and effective avoidance of uncertain state-action spaces. In
addition, we employ meta-learning to train a traversability cost prediction
network. The model can be trained with driving data from a variety of types of
terrain, and it can online-adapt based on interaction experiences to reduce the
aleatoric uncertainty. Integrating the dynamics model and traversability cost
prediction model with a sampling-based model predictive controller allows for
optimizing trajectories that avoid uncertain terrains and state-action spaces.
Experimental results demonstrate that the proposed method reduces uncertainty
in prediction and improves stability in autonomous vehicle navigation in
unstructured environments.

该研究介绍了一种最小化控制和感知不确定性以确保安全可靠的自主车辆导航的框架，由两个不确定性感知模型组成：基于机器学习的车辆动力学模型和自我监督遍历估计模型。

通过控制和感知的不确定性最小化，在非结构化环境中进行安全导航

Safe Navigation in Unstructured Environments by Minimizing Uncertainty  in Control and Perception

Prediction of dynamic environment is crucial to safe navigation of an
autonomous vehicle. Urban traffic scenes are particularly challenging to
forecast due to complex interactions between various dynamic agents, such as
vehicles and vulnerable road users. Previous approaches have used egocentric
occupancy grid maps to represent and predict dynamic environments. However,
these predictions suffer from blurriness, loss of scene structure at turns, and
vanishing of agents over longer prediction horizon. In this work, we propose a
novel framework to make long-term predictions by representing the traffic scene
in a fixed frame, referred as allo-centric occupancy grid. This allows for the
static scene to remain fixed and to represent motion of the ego-vehicle on the
grid like other agents'. We study the allo-centric grid prediction with
different video prediction networks and validate the approach on the real-world
Nuscenes dataset. The results demonstrate that the allo-centric grid
representation significantly improves scene prediction, in comparison to the
conventional ego-centric grid approach.

通过使用固定框架中的全中心占据网格来代表交通状况，以提高对自主车辆动态环境的预测精度，相比于传统的自车中心占据网格方法，全中心占据网格方法能有效地改善场景的预测。