5,289 research outputs found
Towards Vehicle-to-everything Autonomous Driving: A Survey on Collaborative Perception
Vehicle-to-everything (V2X) autonomous driving opens up a promising direction
for developing a new generation of intelligent transportation systems.
Collaborative perception (CP) as an essential component to achieve V2X can
overcome the inherent limitations of individual perception, including occlusion
and long-range perception. In this survey, we provide a comprehensive review of
CP methods for V2X scenarios, bringing a profound and in-depth understanding to
the community. Specifically, we first introduce the architecture and workflow
of typical V2X systems, which affords a broader perspective to understand the
entire V2X system and the role of CP within it. Then, we thoroughly summarize
and analyze existing V2X perception datasets and CP methods. Particularly, we
introduce numerous CP methods from various crucial perspectives, including
collaboration stages, roadside sensors placement, latency compensation,
performance-bandwidth trade-off, attack/defense, pose alignment, etc. Moreover,
we conduct extensive experimental analyses to compare and examine current CP
methods, revealing some essential and unexplored insights. Specifically, we
analyze the performance changes of different methods under different
bandwidths, providing a deep insight into the performance-bandwidth trade-off
issue. Also, we examine methods under different LiDAR ranges. To study the
model robustness, we further investigate the effects of various simulated
real-world noises on the performance of different CP methods, covering
communication latency, lossy communication, localization errors, and mixed
noises. In addition, we look into the sim-to-real generalization ability of
existing CP methods. At last, we thoroughly discuss issues and challenges,
highlighting promising directions for future efforts. Our codes for
experimental analysis will be public at
https://github.com/memberRE/Collaborative-Perception.Comment: 19 page
SEIP: Simulation-based Design and Evaluation of Infrastructure-based Collective Perception
Infrastructure-based collective perception, which entails the real-time
sharing and merging of sensing data from different roadside sensors for object
detection, has shown promise in preventing occlusions for traffic safety and
efficiency. However, its adoption has been hindered by the lack of guidance for
roadside sensor placement and high costs for ex-post evaluation. For
infrastructure projects with limited budgets, the ex-ante evaluation for
optimizing the configurations and placements of infrastructure sensors is
crucial to minimize occlusion risks at a low cost.
This paper presents algorithms and simulation tools to support the ex-ante
evaluation of the cost-performance tradeoff in infrastructure sensor deployment
for collective perception. More specifically, the deployment of infrastructure
sensors is framed as an integer programming problem that can be efficiently
solved in polynomial time, achieving near-optimal results with the use of
certain heuristic algorithms. The solutions provide guidance on deciding sensor
locations, installation heights, and configurations to achieve the balance
between procurement cost, physical constraints for installation, and sensing
coverage. Additionally, we implement the proposed algorithms in a simulation
engine. This allows us to evaluate the effectiveness of each sensor deployment
solution through the lens of object detection. The application of the proposed
methods was illustrated through a case study on traffic monitoring by using
infrastructure LiDARs. Preliminary findings indicate that when working with a
tight sensing budget, it is possible that the incremental benefit derived from
integrating additional low-resolution LiDARs could surpass that of
incorporating more high-resolution ones. The results reinforce the necessity of
investigating the cost-performance tradeoff
iDriving: Toward Safe and Efficient Infrastructure-directed Autonomous Driving
Autonomous driving will become pervasive in the coming decades. iDriving
improves the safety of autonomous driving at intersections and increases
efficiency by improving traffic throughput at intersections. In iDriving,
roadside infrastructure remotely drives an autonomous vehicle at an
intersection by offloading perception and planning from the vehicle to roadside
infrastructure. To achieve this, iDriving must be able to process voluminous
sensor data at full frame rate with a tail latency of less than 100 ms, without
sacrificing accuracy. We describe algorithms and optimizations that enable it
to achieve this goal using an accurate and lightweight perception component
that reasons on composite views derived from overlapping sensors, and a planner
that jointly plans trajectories for multiple vehicles. In our evaluations,
iDriving always ensures safe passage of vehicles, while autonomous driving can
only do so 27% of the time. iDriving also results in 5x lower wait times than
other approaches because it enables traffic-light free intersections
The OpenCDA Open-source Ecosystem for Cooperative Driving Automation Research
Advances in Single-vehicle intelligence of automated driving have encountered
significant challenges because of limited capabilities in perception and
interaction with complex traffic environments. Cooperative Driving
Automation~(CDA) has been considered a pivotal solution to next-generation
automated driving and intelligent transportation. Though CDA has attracted much
attention from both academia and industry, exploration of its potential is
still in its infancy. In industry, companies tend to build their in-house data
collection pipeline and research tools to tailor their needs and protect
intellectual properties. Reinventing the wheels, however, wastes resources and
limits the generalizability of the developed approaches since no standardized
benchmarks exist. On the other hand, in academia, due to the absence of
real-world traffic data and computation resources, researchers often
investigate CDA topics in simplified and mostly simulated environments,
restricting the possibility of scaling the research outputs to real-world
scenarios. Therefore, there is an urgent need to establish an open-source
ecosystem~(OSE) to address the demands of different communities for CDA
research, particularly in the early exploratory research stages, and provide
the bridge to ensure an integrated development and testing pipeline that
diverse communities can share. In this paper, we introduce the OpenCDA research
ecosystem, a unified OSE integrated with a model zoo, a suite of driving
simulators at various resolutions, large-scale real-world and simulated
datasets, complete development toolkits for benchmark training/testing, and a
scenario database/generator. We also demonstrate the effectiveness of OpenCDA
OSE through example use cases, including cooperative 3D LiDAR detection,
cooperative merge, cooperative camera-based map prediction, and adversarial
scenario generation
People tracking by cooperative fusion of RADAR and camera sensors
Accurate 3D tracking of objects from monocular camera poses challenges due to the loss of depth during projection. Although ranging by RADAR has proven effective in highway environments, people tracking remains beyond the capability of single sensor systems. In this paper, we propose a cooperative RADAR-camera fusion method for people tracking on the ground plane. Using average person height, joint detection likelihood is calculated by back-projecting detections from the camera onto the RADAR Range-Azimuth data. Peaks in the joint likelihood, representing candidate targets, are fed into a Particle Filter tracker. Depending on the association outcome, particles are updated using the associated detections (Tracking by Detection), or by sampling the raw likelihood itself (Tracking Before Detection). Utilizing the raw likelihood data has the advantage that lost targets are continuously tracked even if the camera or RADAR signal is below the detection threshold. We show that in single target, uncluttered environments, the proposed method entirely outperforms camera-only tracking. Experiments in a real-world urban environment also confirm that the cooperative fusion tracker produces significantly better estimates, even in difficult and ambiguous situations
Analyzing Infrastructure LiDAR Placement with Realistic LiDAR Simulation Library
Recently, Vehicle-to-Everything(V2X) cooperative perception has attracted
increasing attention. Infrastructure sensors play a critical role in this
research field; however, how to find the optimal placement of infrastructure
sensors is rarely studied. In this paper, we investigate the problem of
infrastructure sensor placement and propose a pipeline that can efficiently and
effectively find optimal installation positions for infrastructure sensors in a
realistic simulated environment. To better simulate and evaluate LiDAR
placement, we establish a Realistic LiDAR Simulation library that can simulate
the unique characteristics of different popular LiDARs and produce
high-fidelity LiDAR point clouds in the CARLA simulator. Through simulating
point cloud data in different LiDAR placements, we can evaluate the perception
accuracy of these placements using multiple detection models. Then, we analyze
the correlation between the point cloud distribution and perception accuracy by
calculating the density and uniformity of regions of interest. Experiments show
that when using the same number and type of LiDAR, the placement scheme
optimized by our proposed method improves the average precision by 15%,
compared with the conventional placement scheme in the standard lane scene. We
also analyze the correlation between perception performance in the region of
interest and LiDAR point cloud distribution and validate that density and
uniformity can be indicators of performance. Both the RLS Library and related
code will be released at
https://github.com/PJLab-ADG/LiDARSimLib-and-Placement-Evaluation.Comment: 7 pages, 6 figures, accepted to the IEEE International Conference on
Robotics and Automation (ICRA'23
Collaborative Perception in Autonomous Driving: Methods, Datasets and Challenges
Collaborative perception is essential to address occlusion and sensor failure
issues in autonomous driving. In recent years, theoretical and experimental
investigations of novel works for collaborative perception have increased
tremendously. So far, however, few reviews have focused on systematical
collaboration modules and large-scale collaborative perception datasets. This
work reviews recent achievements in this field to bridge this gap and motivate
future research. We start with a brief overview of collaboration schemes. After
that, we systematically summarize the collaborative perception methods for
ideal scenarios and real-world issues. The former focuses on collaboration
modules and efficiency, and the latter is devoted to addressing the problems in
actual application. Furthermore, we present large-scale public datasets and
summarize quantitative results on these benchmarks. Finally, we highlight gaps
and overlook challenges between current academic research and real-world
applications. The project page is
https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-DrivingComment: 18 pages, 6 figures. Accepted by IEEE Intelligent Transportation
Systems Magazine. URL:
https://github.com/CatOneTwo/Collaborative-Perception-in-Autonomous-Drivin
Towards the simulation of cooperative perception applications by leveraging distributed sensing infrastructures
With the rapid development of Automated Vehicles (AV), the boundaries of their function alities are being pushed and new challenges are being imposed. In increasingly complex
and dynamic environments, it is fundamental to rely on more powerful onboard sensors and
usually AI. However, there are limitations to this approach. As AVs are increasingly being
integrated in several industries, expectations regarding their cooperation ability is growing,
and vehicle-centric approaches to sensing and reasoning, become hard to integrate. The
proposed approach is to extend perception to the environment, i.e. outside of the vehicle,
by making it smarter, via the deployment of wireless sensors and actuators. This will vastly
improve the perception capabilities in dynamic and unpredictable scenarios and often in a
cheaper way, relying mostly in the use of lower cost sensors and embedded devices, which rely
on their scale deployment instead of centralized sensing abilities. Consequently, to support
the development and deployment of such cooperation actions in a seamless way, we require
the usage of co-simulation frameworks, that can encompass multiple perspectives of control
and communications for the AVs, the wireless sensors and actuators and other actors in the
environment. In this work, we rely on ROS2 and micro-ROS as the underlying technologies
for integrating several simulation tools, to construct a framework, capable of supporting the
development, test and validation of such smart, cooperative environments. This endeavor
was undertaken by building upon an existing simulation framework known as AuNa. We
extended its capabilities to facilitate the simulation of cooperative scenarios by incorporat ing external sensors placed within the environment rather than just relying on vehicle-based
sensors. Moreover, we devised a cooperative perception approach within this framework,
showcasing its substantial potential and effectiveness. This will enable the demonstration of
multiple cooperation scenarios and also ease the deployment phase by relying on the same
software architecture.Com o rápido desenvolvimento dos Veículos Autónomos (AV), os limites das suas funcional idades estão a ser alcançados e novos desafios estão a surgir. Em ambientes complexos
e dinâmicos, é fundamental a utilização de sensores de alta capacidade e, na maioria dos
casos, inteligência artificial. Mas existem limitações nesta abordagem. Como os AVs estão
a ser integrados em várias indústrias, as expectativas quanto à sua capacidade de cooperação estão a aumentar, e as abordagens de perceção e raciocínio centradas no veículo,
tornam-se difíceis de integrar. A abordagem proposta consiste em extender a perceção para
o ambiente, isto é, fora do veículo, tornando-a inteligente, através do uso de sensores e
atuadores wireless. Isto irá melhorar as capacidades de perceção em cenários dinâmicos e
imprevisíveis, reduzindo o custo, pois a abordagem será baseada no uso de sensores low-cost
e sistemas embebidos, que dependem da sua implementação em grande escala em vez da
capacidade de perceção centralizada. Consequentemente, para apoiar o desenvolvimento
e implementação destas ações em cooperação, é necessária a utilização de frameworks de
co-simulação, que abranjam múltiplas perspetivas de controlo e comunicação para os AVs,
sensores e atuadores wireless, e outros atores no ambiente. Neste trabalho será utilizado
ROS2 e micro-ROS como as tecnologias subjacentes para a integração das ferramentas de
simulação, de modo a construir uma framework capaz de apoiar o desenvolvimento, teste e
validação de ambientes inteligentes e cooperativos. Esta tarefa foi realizada com base numa
framework de simulação denominada AuNa. Foram expandidas as suas capacidades para
facilitar a simulação de cenários cooperativos através da incorporação de sensores externos
colocados no ambiente, em vez de depender apenas de sensores montados nos veículos.
Além disso, concebemos uma abordagem de perceção cooperativa usando a framework,
demonstrando o seu potencial e eficácia. Isto irá permitir a demonstração de múltiplos
cenários de cooperação e também facilitar a fase de implementação, utilizando a mesma
arquitetura de software
Automated Driving Systems Data Acquisition and Processing Platform
This paper presents an automated driving system (ADS) data acquisition and
processing platform for vehicle trajectory extraction, reconstruction, and
evaluation based on connected automated vehicle (CAV) cooperative perception.
This platform presents a holistic pipeline from the raw advanced sensory data
collection to data processing, which can process the sensor data from multiple
CAVs and extract the objects' Identity (ID) number, position, speed, and
orientation information in the map and Frenet coordinates. First, the ADS data
acquisition and analytics platform are presented. Specifically, the
experimental CAVs platform and sensor configuration are shown, and the
processing software, including a deep-learning-based object detection algorithm
using LiDAR information, a late fusion scheme to leverage cooperative
perception to fuse the detected objects from multiple CAVs, and a multi-object
tracking method is introduced. To further enhance the object detection and
tracking results, high definition maps consisting of point cloud and vector
maps are generated and forwarded to a world model to filter out the objects off
the road and extract the objects' coordinates in Frenet coordinates and the
lane information. In addition, a post-processing method is proposed to refine
trajectories from the object tracking algorithms. Aiming to tackle the ID
switch issue of the object tracking algorithm, a fuzzy-logic-based approach is
proposed to detect the discontinuous trajectories of the same object. Finally,
results, including object detection and tracking and a late fusion scheme, are
presented, and the post-processing algorithm's improvements in noise level and
outlier removal are discussed, confirming the functionality and effectiveness
of the proposed holistic data collection and processing platform
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