196 research outputs found
Towards Secure and Safe Appified Automated Vehicles
The advancement in Autonomous Vehicles (AVs) has created an enormous market
for the development of self-driving functionalities,raising the question of how
it will transform the traditional vehicle development process. One adventurous
proposal is to open the AV platform to third-party developers, so that AV
functionalities can be developed in a crowd-sourcing way, which could provide
tangible benefits to both automakers and end users. Some pioneering companies
in the automotive industry have made the move to open the platform so that
developers are allowed to test their code on the road. Such openness, however,
brings serious security and safety issues by allowing untrusted code to run on
the vehicle. In this paper, we introduce the concept of an Appified AV platform
that opens the development framework to third-party developers. To further
address the safety challenges, we propose an enhanced appified AV design schema
called AVGuard, which focuses primarily on mitigating the threats brought about
by untrusted code, leveraging theory in the vehicle evaluation field, and
conducting program analysis techniques in the cybersecurity area. Our study
provides guidelines and suggested practice for the future design of open AV
platforms
The Mason Test: A Defense Against Sybil Attacks in Wireless Networks Without Trusted Authorities
Wireless networks are vulnerable to Sybil attacks, in which a malicious node
poses as many identities in order to gain disproportionate influence. Many
defenses based on spatial variability of wireless channels exist, but depend
either on detailed, multi-tap channel estimation - something not exposed on
commodity 802.11 devices - or valid RSSI observations from multiple trusted
sources, e.g., corporate access points - something not directly available in ad
hoc and delay-tolerant networks with potentially malicious neighbors. We extend
these techniques to be practical for wireless ad hoc networks of commodity
802.11 devices. Specifically, we propose two efficient methods for separating
the valid RSSI observations of behaving nodes from those falsified by malicious
participants. Further, we note that prior signalprint methods are easily
defeated by mobile attackers and develop an appropriate challenge-response
defense. Finally, we present the Mason test, the first implementation of these
techniques for ad hoc and delay-tolerant networks of commodity 802.11 devices.
We illustrate its performance in several real-world scenarios
ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency
Deep neural networks (DNNs) are increasingly integrated into LiDAR (Light
Detection and Ranging)-based perception systems for autonomous vehicles (AVs),
requiring robust performance under adversarial conditions. We aim to address
the challenge of LiDAR spoofing attacks, where attackers inject fake objects
into LiDAR data and fool AVs to misinterpret their environment and make
erroneous decisions. However, current defense algorithms predominantly depend
on perception outputs (i.e., bounding boxes) thus face limitations in detecting
attackers given the bounding boxes are generated by imperfect perception models
processing limited points, acquired based on the ego vehicle's viewpoint. To
overcome these limitations, we propose a novel framework, named ADoPT (Anomaly
Detection based on Point-level Temporal consistency), which quantitatively
measures temporal consistency across consecutive frames and identifies abnormal
objects based on the coherency of point clusters. In our evaluation using the
nuScenes dataset, our algorithm effectively counters various LiDAR spoofing
attacks, achieving a low ( 85%)
true positive ratio (TPR), outperforming existing state-of-the-art defense
methods, CARLO and 3D-TC2. Furthermore, our evaluation demonstrates the
promising potential for accurate attack detection across various road
environments.Comment: BMVC 2023 (17 pages, 13 figures, and 1 table
CALICO: Self-Supervised Camera-LiDAR Contrastive Pre-training for BEV Perception
Perception is crucial in the realm of autonomous driving systems, where
bird's eye view (BEV)-based architectures have recently reached
state-of-the-art performance. The desirability of self-supervised
representation learning stems from the expensive and laborious process of
annotating 2D and 3D data. Although previous research has investigated
pretraining methods for both LiDAR and camera-based 3D object detection, a
unified pretraining framework for multimodal BEV perception is missing. In this
study, we introduce CALICO, a novel framework that applies contrastive
objectives to both LiDAR and camera backbones. Specifically, CALICO
incorporates two stages: point-region contrast (PRC) and region-aware
distillation (RAD). PRC better balances the region- and scene-level
representation learning on the LiDAR modality and offers significant
performance improvement compared to existing methods. RAD effectively achieves
contrastive distillation on our self-trained teacher model. CALICO's efficacy
is substantiated by extensive evaluations on 3D object detection and BEV map
segmentation tasks, where it delivers significant performance improvements.
Notably, CALICO outperforms the baseline method by 10.5% and 8.6% on NDS and
mAP. Moreover, CALICO boosts the robustness of multimodal 3D object detection
against adversarial attacks and corruption. Additionally, our framework can be
tailored to different backbones and heads, positioning it as a promising
approach for multimodal BEV perception
A Cooperative Perception Environment for Traffic Operations and Control
Existing data collection methods for traffic operations and control usually
rely on infrastructure-based loop detectors or probe vehicle trajectories.
Connected and automated vehicles (CAVs) not only can report data about
themselves but also can provide the status of all detected surrounding
vehicles. Integration of perception data from multiple CAVs as well as
infrastructure sensors (e.g., LiDAR) can provide richer information even under
a very low penetration rate. This paper aims to develop a cooperative data
collection system, which integrates Lidar point cloud data from both
infrastructure and CAVs to create a cooperative perception environment for
various transportation applications. The state-of-the-art 3D detection models
are applied to detect vehicles in the merged point cloud. We test the proposed
cooperative perception environment with the max pressure adaptive signal
control model in a co-simulation platform with CARLA and SUMO. Results show
that very low penetration rates of CAV plus an infrastructure sensor are
sufficient to achieve comparable performance with 30% or higher penetration
rates of connected vehicles (CV). We also show the equivalent CV penetration
rate (E-CVPR) under different CAV penetration rates to demonstrate the data
collection efficiency of the cooperative perception environment
Mobile network performance from user devices: A longitudinal, multidimensional analysis
Abstract. In the cellular environment, operators, researchers and end users have poor visibility into network performance for devices. Improving visibility is challenging because this performance depends factors that include carrier, access technology, signal strength, geographic location and time. Addressing this requires longitudinal, continuous and large-scale measurements from a diverse set of mobile devices and networks. This paper takes a first look at cellular network performance from this perspective, using 17 months of data collected from devices located throughout the world. We show that (i) there is significant variance in key performance metrics both within and across carriers; (ii) this variance is at best only partially explained by regional and time-of-day patterns; (iii) the stability of network performance varies substantially among carriers. Further, we use the dataset to diagnose the causes behind observed performance problems and identify additional measurements that will improve our ability to reason about mobile network behavior
RadioProphet: Intelligent Radio Resource Deallocation for Cellular Networks
Abstract. Traditionally, radio resources are released in cellular networks by statically configured inactivity timers, causing substantial resource inefficiencies. We propose a novel system RadioProphet (RP), which dynamically and intelligently determines in real time when to deallocate radio resources by predicting the network idle time based on traffic history. We evaluate RP using 7-month-long real-world cellular traces. Properly configured, RP correctly predicts 85.9 % of idle time instances and achieves radio energy savings of 59.1 % at the cost of 91.0 % of signaling overhead, outperforming existing proposals. We also implement and evaluate RP on real Android devices, demonstrating its negligible runtime overhead.
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