Anomaly Detection in Connected and Automated Vehicles using an Augmented State Formulation
University of Michigan（密歇根大学）；University of Tennessee Knoxville（田纳西大学诺克斯维尔分校）
Yiyang Wang, Neda Masoud, Anahita Khojandi
备注：
Accepted to be Published in: 2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Delft, Netherlands, 2020, pp. 156-161. arXiv admin note: text overlap with arXiv:1911.01531
发表信息：
2020 Forum on Integrated and Sustainable Transportation Systems (FISTS), Delft, Netherlands, 2020, pp. 156-161
In this paper we propose a novel observer-based method for anomaly detection in connected and automated vehicles (CAVs). The proposed method utilizes an augmented extended Kalman filter (AEKF) to smooth sensor readings of a CAV based on a nonlinear car-following motion model with time delay, where the leading vehicle's trajectory is used by the subject vehicle to detect sensor anomalies. We use the classic $χ^2$ fault detector in conjunction with the proposed AEKF for anomaly detection. To make the proposed model more suitable for real-world applications, we consider a stochastic communication time delay in the car-following model. Our experiments conducted on real-world connected vehicle data indicate that the AEKF with $χ^2$-detector can achieve a high anomaly detection performance.
In autonomous driving, detection of abnormal driving behaviors is essential to ensure the safety of vehicle controllers. Prior works in vehicle anomaly detection have shown that modeling interactions between agents improves detection accuracy, but certain abnormal behaviors where structured road information is paramount are poorly identified, such as wrong-way and off-road driving. We propose a novel unsupervised framework for highway anomaly detection named Structural Attention-Based Recurrent VAE (SABeR-VAE), which explicitly uses the structure of the environment to aid anomaly identificatio…
Harvard University（哈佛大学）；Georgia Institute of Technology（佐治亚理工学院）；Peking University（北京大学）
Safety and resilience are critical for autonomous unmanned aerial vehicles (UAVs). We introduce MAVFI, the micro aerial vehicles (MAVs) resilience analysis methodology to assess the effect of silent data corruption (SDC) on UAVs' mission metrics, such as flight time and success rate, for accurately measuring system resilience. To enhance the safety and resilience of robot systems bound by size, weight, and power (SWaP), we offer two low-overhead anomaly-based SDC detection and recovery algorithms based on Gaussian statistical models and autoencoder neural networks. Our anomaly error protection…
Evaluation of Large Language Models for Anomaly Detection in Autonomous Vehicles
Democritus University of Thrace（德米特里乌斯大学）；Dongguan University of Technology（东莞理工大学）；ENS-Paris-Saclay University（巴黎-萨克雷大学）；Neapolis University Pafos（纳皮奥斯大学）
Universidade Federal do Espírito Santo（圣埃斯皮里图联邦大学）
We propose a bio-inspired foveated technique to detect cars in a long range camera view using a deep convolutional neural network (DCNN) for the IARA self-driving car. The DCNN receives as input (i) an image, which is captured by a camera installed on IARA's roof; and (ii) crops of the image, which are centered in the waypoints computed by IARA's path planner and whose sizes increase with the distance from IARA. We employ an overlap filter to discard detections of the same car in different crops of the same image based on the percentage of overlap of detections' bounding boxes. We evaluated th…
Robotics Institute, Carnegie Mellon University（卡内基梅隆大学机器人研究所）
The recent increase in the use of aerial vehicles raises concerns about the safety and reliability of autonomous operations. There is a growing need for methods to monitor the status of these aircraft and report any faults and anomalies to the safety pilot or to the autopilot to deal with the emergency situation. In this paper, we present a real-time approach using the Recursive Least Squares method to detect anomalies in the behavior of an aircraft. The method models the relationship between correlated input-output pairs online and uses the model to detect anomalies. The result is an easy-to-…
A Semantic Observer Layer for Autonomous Vehicles: Pre-Deployment Feasibility Study of VLMs for Low-Latency Anomaly Detection
The City College of New York, Grove School of Engineering（纽约市立学院格罗夫工程学院）；Department of Mechanical and Aerospace Engineering, Tandon School of Engineering, New York University（纽约大学坦登工程学院机械与航空航天工程系）；Department of Electrical and Computer Engineering, Tandon School of Engineering, New York University（纽约大学坦登工程学院电气与计算机工程系）
ALT-Pilot: Autonomous navigation with Language augmented Topometric maps
The University of Texas at Austin（德克萨斯大学奥斯汀分校）；International Institute of Information Technology, Hyderabad（海得拉巴国际信息技术学院）；Massachusetts Institute of Technology（麻省理工学院）
The paper proposes an on-line monitoring framework for continuous real-time safety/security in learning-based control systems (specifically application to a unmanned ground vehicle). We monitor validity of mappings from sensor inputs to actuator commands, controller-focused anomaly detection (CFAM), and from actuator commands to sensor inputs, system-focused anomaly detection (SFAM). CFAM is an image conditioned energy based generative adversarial network (EBGAN) in which the energy based discriminator distinguishes between proper and anomalous actuator commands. SFAM is based on an action con…
Michigan Technological University（密歇根理工大学）；University of Science and Technology of China（中国科学技术大学）
Despite the success of AI-enabled onboard perception, on-ramp merging has been one of the main challenges for autonomous driving. Due to limited sensing range of onboard sensors, a merging vehicle can hardly observe main road conditions and merge properly. By leveraging the wireless communications between connected and automated vehicles (CAVs), a merging CAV has potential to proactively obtain the intentions of nearby vehicles. However, CAVs can be prone to inaccurate observations, such as the noisy basic safety messages (BSM) and poor quality surveillance images. In this paper, we present a …
