86 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
Many-Objective Reinforcement Learning for Online Testing of DNN-Enabled Systems
Deep Neural Networks (DNNs) have been widely used to perform real-world tasks
in cyber-physical systems such as Autonomous Diving Systems (ADS). Ensuring the
correct behavior of such DNN-Enabled Systems (DES) is a crucial topic. Online
testing is one of the promising modes for testing such systems with their
application environments (simulated or real) in a closed loop taking into
account the continuous interaction between the systems and their environments.
However, the environmental variables (e.g., lighting conditions) that might
change during the systems' operation in the real world, causing the DES to
violate requirements (safety, functional), are often kept constant during the
execution of an online test scenario due to the two major challenges: (1) the
space of all possible scenarios to explore would become even larger if they
changed and (2) there are typically many requirements to test simultaneously.
In this paper, we present MORLOT (Many-Objective Reinforcement Learning for
Online Testing), a novel online testing approach to address these challenges by
combining Reinforcement Learning (RL) and many-objective search. MORLOT
leverages RL to incrementally generate sequences of environmental changes while
relying on many-objective search to determine the changes so that they are more
likely to achieve any of the uncovered objectives. We empirically evaluate
MORLOT using CARLA, a high-fidelity simulator widely used for autonomous
driving research, integrated with Transfuser, a DNN-enabled ADS for end-to-end
driving. The evaluation results show that MORLOT is significantly more
effective and efficient than alternatives with a large effect size. In other
words, MORLOT is a good option to test DES with dynamically changing
environments while accounting for multiple safety requirements
Communication Resources Constrained Hierarchical Federated Learning for End-to-End Autonomous Driving
While federated learning (FL) improves the generalization of end-to-end
autonomous driving by model aggregation, the conventional single-hop FL (SFL)
suffers from slow convergence rate due to long-range communications among
vehicles and cloud server. Hierarchical federated learning (HFL) overcomes such
drawbacks via introduction of mid-point edge servers. However, the
orchestration between constrained communication resources and HFL performance
becomes an urgent problem. This paper proposes an optimization-based
Communication Resource Constrained Hierarchical Federated Learning (CRCHFL)
framework to minimize the generalization error of the autonomous driving model
using hybrid data and model aggregation. The effectiveness of the proposed
CRCHFL is evaluated in the Car Learning to Act (CARLA) simulation platform.
Results show that the proposed CRCHFL both accelerates the convergence rate and
enhances the generalization of federated learning autonomous driving model.
Moreover, under the same communication resource budget, it outperforms the HFL
by 10.33% and the SFL by 12.44%
Teachers as co-designers of technology-rich learning activities for emergent literacy
Although kindergarten teachers often struggle with implementing technology, they are rarely involved in co-designing technology-rich learning activities. This study involved teachers in the co-design of technology-rich learning activities and sought to explore implementation and pupil learning outcomes. A case-study method was used to investigate: the co-design experiences of seven teachers; implementation in three kindergarten classes; and pupil learning outcomes. Interviews were used to study teacher perceptions about pedagogy, technology, early literacy, co-designer role, practicality and co-ownership. Process notes were made during design team meetings. Observations were made of implementation, and pupil learning was pre- and post-tested in non-equivalent control quasi experimental design (N = 111). Findings indicate that teacher perceptions about pedagogy affect their co-design involvement. The extent of integration of on- and off-computer activities was similar between teachers. Significant pupil learning gains were found, thus indicating that the co-designed activities had positive effects on pupil learning outcomes
Investigating the Impact of Multi-LiDAR Placement on Object Detection for Autonomous Driving
The past few years have witnessed an increasing interest in improving the
perception performance of LiDARs on autonomous vehicles. While most of the
existing works focus on developing new deep learning algorithms or model
architectures, we study the problem from the physical design perspective, i.e.,
how different placements of multiple LiDARs influence the learning-based
perception. To this end, we introduce an easy-to-compute information-theoretic
surrogate metric to quantitatively and fast evaluate LiDAR placement for 3D
detection of different types of objects. We also present a new data collection,
detection model training and evaluation framework in the realistic CARLA
simulator to evaluate disparate multi-LiDAR configurations. Using several
prevalent placements inspired by the designs of self-driving companies, we show
the correlation between our surrogate metric and object detection performance
of different representative algorithms on KITTI through extensive experiments,
validating the effectiveness of our LiDAR placement evaluation approach. Our
results show that sensor placement is non-negligible in 3D point cloud-based
object detection, which will contribute up to 10% performance discrepancy in
terms of average precision in challenging 3D object detection settings. We
believe that this is one of the first studies to quantitatively investigate the
influence of LiDAR placement on perception performance. The code is available
on https://github.com/HanjiangHu/Multi-LiDAR-Placement-for-3D-Detection.Comment: CVPR 2022 camera-ready version:15 pages, 14 figures, 9 table
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