DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

Bechtel, Michael; Mcellhiney, Elise; Kim, Minje; Yun, Heechul

doi:10.48550/arxiv.1712.08644

Cited by 4 publications

(4 citation statements)

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“…Within a period, if the number of memory access initiated from a core exceeds the budget, MemGuard will restrict memory access from this core until the budget is replenished in the next period. It has been used on DeepPicar [17] to solve resource contention issue.…”

Section: Memory Dos Protectionmentioning

confidence: 99%

A Container-based DoS Attack-Resilient Control Framework for Real-Time UAV Systems

Chen

Feng

Wen

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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The Unmanned aerial vehicles (UAVs) sector is fastexpanding. Protection of real-time UAV applications against malicious attacks has become an urgent problem that needs to be solved. Denial-of-service (DoS) attack aims to exhaust system resources and cause important tasks to miss deadlines. DoS attack may be one of the common problems of UAV systems, due to its simple implementation. In this paper, we present a software framework that offers DoS attack-resilient control for real-time UAV systems using containers: ContainerDrone. The framework provides defense mechanisms for three critical system resources: CPU, memory, and communication channel. We restrict attacker's access to CPU core set and utilization. Memory bandwidth throttling limits attacker's memory usage. By simulating sensors and drivers in the container, a security monitor constantly checks DoS attacks over communication channels. Upon the detection of a security rule violation, the framework switches to the safety controller to mitigate the attack. We implemented a prototype quadcopter with commercially offthe-shelf (COTS) hardware and open-source software. Our experimental results demonstrated the effectiveness of the proposed framework defending against various DoS attacks.

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Section: Memory Dos Protectionmentioning

confidence: 99%

A Container-based DoS Attack-Resilient Control Framework for Real-Time UAV Systems

Chen

Feng

Wen

et al. 2019

2019 Design, Automation &Amp; Test in Europe Conference &Amp; Exhibition (DATE)

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“…$3000 vs. $418). DeepPicar [25] is another platform which is built using a smaller 1/24 scale RC chassis. This platform also uses an RPi3 as the computing unit and performs autonomous driving using NVIDIA's DAVE-II CNN.…”

Section: Related Workmentioning

confidence: 99%

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

Ramakrishna

Dubey

Burruss

et al. 2019

2019 IEEE 22nd International Symposium on Real-Time Distributed Computing (ISORC)

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Learning enabled components (LECs) trained using data-driven algorithms are increasingly being used in autonomous robots commonly found in factories, hospitals, and educational laboratories. However, these LECs do not provide any safety guarantees, and testing them is challenging. In this paper, we introduce a framework that performs weighted simplex strategy based supervised safety control, resource management and confidence estimation of autonomous robots. Specifically, we describe two weighted simplex strategies: (a) simple weighted simplex strategy (SW-Simplex) that computes a weighted controller output by comparing the decisions between a safety supervisor and an LEC, and (b) a context-sensitive weighted simplex strategy (CSW-Simplex) that computes a context-aware weighted controller output. We use reinforcement learning to learn the contextual weights. We also introduce a system monitor that uses the current state information and a Bayesian network model learned from past data to estimate the probability of the robotic system staying in the safe working region. To aid resource constrained robots in performing complex computations of these weighted simplex strategies, we describe a resource manager that offloads tasks to an available fog nodes. The paper also describes a hardware testbed called DeepNNCar, which is a low cost resource-constrained RC car, built to perform autonomous driving. Using the hardware, we show that both SW-Simplex and CSW-Simplex have 40% and 60% fewer safety violations, while demonstrating higher optimized speed during indoor driving (∼ 0.40 m/s) than the original system (using only LECs).

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“…2, consists of a LaTrax Desert Prerunner 1/18-scale radio-controlled (RC) car chassis, an electronic speed controller (ESC), a Raspberry Pi 3 Model B+, and a Raspberry Pi Camera Module v2. It is an adaptation of the DeepPicar [14] but with a focus on integration into a vehicular network rather than evaluating the viability of embedded systems in autonomous vehicles. In addition, our In the autonomous control mode, the control flow consists of three main steps: front-view image streaming to a remote convolutional neural network (CNN) service, CNNbased image processing, and generated turn-angle and speed value streaming to the vehicle.…”

Section: A Vehicle Controlmentioning

confidence: 99%

A Physical Testbed for Intelligent Transportation Systems

Morrissett

Eini

Zaman

et al. 2019

2019 12th International Conference on Human System Interaction (HSI)

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Intelligent transportation systems (ITSs) and other smart-city technologies are increasingly advancing in capability and complexity. While simulation environments continue to improve, their fidelity and ease of use can quickly degrade as newer systems become increasingly complex. To remedy this, we propose a hardware-and software-based traffic management system testbed as part of a larger smart-city testbed. It comprises a network of connected vehicles, a network of intersection controllers, a variety of control services, and data analytics services. The main goal of our testbed is to provide researchers and students with the means to develop novel traffic and vehicle control algorithms with higher fidelity than what can be achieved with simulation alone. Specifically, we are using the testbed to develop an integrated management system that combines model-based control and data analytics to improve the system performance over time. In this paper, we give a detailed description of each component within the testbed and discuss its current developmental state. Additionally, we present initial results and propose future work.

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DeepPicar: A Low-cost Deep Neural Network-based Autonomous Car

Cited by 4 publications

References 0 publications

A Container-based DoS Attack-Resilient Control Framework for Real-Time UAV Systems

A Container-based DoS Attack-Resilient Control Framework for Real-Time UAV Systems

Augmenting Learning Components for Safety in Resource Constrained Autonomous Robots

A Physical Testbed for Intelligent Transportation Systems

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