Computing Systems for Autonomous Driving: State of the Art and Challenges

Liu, Liangkai; Lu, Sidi; Ren, Zhong; Wu, Baofu; Yao, Yongtao; Zhang, Qingyang; Shi, Wei

doi:10.1109/jiot.2020.3043716

Cited by 163 publications

(47 citation statements)

References 148 publications

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“…Future applications for fully Connected and Autonomous Vehicles (CAVs) include an AR-based navigation system, a video-conferencing application, real-time image-processing and inferencing solutions implemented by a neural network model equipped on board [6,7]. Since these applications require a high level of computation power and low power consumption, various hardware implementations such as a Graphics Processing Unit (GPU), field-programmable gate array (FPGA), and Application-Specific Integrated Circuits (ASIC) are employed.…”

Section: Introductionmentioning

confidence: 99%

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

Hong¹,

Hwang²,

Kim³

et al. 2021

Applied Sciences

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An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

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Section: Introductionmentioning

confidence: 99%

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

Hong¹,

Hwang²,

Kim³

et al. 2021

Applied Sciences

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“…Secondly, computer vision problems are a central part of autonomous systems research, and therefore of broad interest to the real-time machine learning community. The authors in [8] highlight the importance of timeliness for safety in computing systems for autonomous driving. Autonomous vehicles take in a myriad of real-time data which needs to be processed and assessed with deep learning algorithms.…”

Section: Introductionmentioning

confidence: 99%

On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Springer¹,

Eiroa-Lledo²,

Stevens³

et al. 2021

Electronics

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As machine learning becomes ubiquitous, the need to deploy models on real-time, embedded systems will become increasingly critical. This is especially true for deep learning solutions, whose large models pose interesting challenges for target architectures at the “edge” that are resource-constrained. The realization of machine learning, and deep learning, is being driven by the availability of specialized hardware, such as system-on-chip solutions, which provide some alleviation of constraints. Equally important, however, are the operating systems that run on this hardware, and specifically the ability to leverage commercial real-time operating systems which, unlike general purpose operating systems such as Linux, can provide the low-latency, deterministic execution required for embedded, and potentially safety-critical, applications at the edge. Despite this, studies considering the integration of real-time operating systems, specialized hardware, and machine learning/deep learning algorithms remain limited. In particular, better mechanisms for real-time scheduling in the context of machine learning applications will prove to be critical as these technologies move to the edge. In order to address some of these challenges, we present a resource management framework designed to provide a dynamic on-device approach to the allocation and scheduling of limited resources in a real-time processing environment. These types of mechanisms are necessary to support the deterministic behavior required by the control components contained in the edge nodes. To validate the effectiveness of our approach, we applied rigorous schedulability analysis to a large set of randomly generated simulated task sets and then verified the most time critical applications, such as the control tasks which maintained low-latency deterministic behavior even during off-nominal conditions. The practicality of our scheduling framework was demonstrated by integrating it into a commercial real-time operating system (VxWorks) then running a typical deep learning image processing application to perform simple object detection. The results indicate that our proposed resource management framework can be leveraged to facilitate integration of machine learning algorithms with real-time operating systems and embedded platforms, including widely-used, industry-standard real-time operating systems.

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“…There are studies in which the control function is considered as an end-to-end solution [4] implemented with deep neural networks using reinforcement learning. This approach has a significant drawback-it has a low explanatory feature in decision making [6]. This is a very important aspect of developing an autonomous vehicle control system, because people must understand the criteria for making decisions about traffic and agree with them, and when analyzing an accident, it is important to find out the causes of the accident [7].…”

Section: Introductionmentioning

confidence: 99%

“…An end-to-end solution [8] also seems unlikely due to the complexity of the problem being solved and the low performance of computing systems that can now be used on board, as well as the tendency of neural networks to overfit. The classical approach [6] involves the creation of a control system based on data flow control with feedback. This approach [9] is even more difficult to implement, but the data generated at each stage of processing can be explained.…”

Section: Introductionmentioning

confidence: 99%

Functional Model of a Self-Driving Car Control System

et al. 2021

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The article describes a structural and functional model of a self-driving car control system, which generates a wide class of mathematical problems. Currently, control systems for self-driving cars are considered at several levels of abstraction and implementation: Mechanics, electronics, perception, scene recognition, control, security, integration of all subsystems into a solid system. Modern research often considers particular problems to be solved for each of the levels separately. In this paper, a parameterized model of the integration of individual components into a complex control system for a self-driving car is considered. Such a model simplifies the design and development of self-driving control systems with configurable automation tools, taking into account the specifics of the solving problem. The parameterized model can be used for CAD design in the field of self-driving car development. A full cycle of development of a control system for a self-driving truck was implemented, which was rub in the “Robocross 2021” competition. The software solution was tested on more than 40 launches of a self-driving truck. Parameterization made it possible to speed up the development of the control system, expressed in man-hours, by 1.5 times compared to the experience of the authors of the article who participated in the same competition in 2018 and 2019. The proposed parameterization was used in the development of individual CAD elements described in this article. Additionally, the implementation of specific modules and functions is a field for experimental research.

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Computing Systems for Autonomous Driving: State of the Art and Challenges

Cited by 163 publications

References 148 publications

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

On-Device Deep Learning Inference for System-on-Chip (SoC) Architectures

Functional Model of a Self-Driving Car Control System

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