Architecture Design and Implementation of an Autonomous Vehicle

Zong, Wenhao; Zhang, Changzhu; Wang, Zhuping; Zhu, Jian Kang; Chen, Qijun

doi:10.1109/access.2018.2828260

Cited by 58 publications

(28 citation statements)

References 46 publications

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“…In [14], the architecture design and implementation of an AV is discussed, and, in [15], various strategies of multi-sensor fusion are discussed. The recent developments and advancements in the perception and sensor technologies for AVs are presented in [16].…”

Section: Literature Reviewmentioning

confidence: 99%

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar

Ahmed

Khan

et al. 2021

Sensors

221

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The Department of Transport in the United Kingdom recorded 25,080 motor vehicle fatalities in 2019. This situation stresses the need for an intelligent transport system (ITS) that improves road safety and security by avoiding human errors with the use of autonomous vehicles (AVs). Therefore, this survey discusses the current development of two main components of an ITS: (1) gathering of AVs surrounding data using sensors; and (2) enabling vehicular communication technologies. First, the paper discusses various sensors and their role in AVs. Then, various communication technologies for AVs to facilitate vehicle to everything (V2X) communication are discussed. Based on the transmission range, these technologies are grouped into three main categories: long-range, medium-range and short-range. The short-range group presents the development of Bluetooth, ZigBee and ultra-wide band communication for AVs. The medium-range examines the properties of dedicated short-range communications (DSRC). Finally, the long-range group presents the cellular-vehicle to everything (C-V2X) and 5G-new radio (5G-NR). An important characteristic which differentiates each category and its suitable application is latency. This research presents a comprehensive study of AV technologies and identifies the main advantages, disadvantages, and challenges.

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Section: Literature Reviewmentioning

confidence: 99%

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Ahangar

Ahmed

Khan

et al. 2021

Sensors

221

View full text Add to dashboard Cite

show abstract

“…For the path tracking problem in autonomous driving, it is of great significance to obtain the relative distance between the vehicle position and the road to follow, i.e., the path tracking error or lateral offset. In general, several methods are utilized to compute the tracking error, such as, GPS, SLAM via Lida sensor and/or camera [38]. As the present work is focused on steering control for path tracking in lower-level layer in the structure of autonomous driving, we assume that the tracking errors in a preview distance l s are available as computed by upper perception module, and its dynamics can be expressed as [21], [25]:ẏ…”

Section: A Vehicle Lateral Dynamicsmentioning

confidence: 99%

Fuzzy-Model-Based Output Feedback Steering Control in Autonomous Driving Subject to Actuator Constraints

Zhang

Lam

Qiu

et al. 2021

IEEE Trans. Fuzzy Syst.

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“…A common approach to designing an autonomous vehicle is to build layers to abstract the decision making that happens in planning and controlling a vehicle, cf. [17] [18]. At a high level shown in figure 1, the planning layers use the world model generated by perception tasks to decide on a route, choose behaviors such as when to change lanes and then plan a path in the world.…”

Section: Perception As Prediction In Autonomous Drivingmentioning

confidence: 99%

Perception as prediction using general value functions in autonomous driving applications

Graves

Rezaee

Scheideman

2019

2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

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We propose and demonstrate a framework called perception as prediction for autonomous driving that uses general value functions (GVFs) to learn predictions. Perception as prediction learns data-driven predictions relating to the impact of actions on the agent's perception of the world. It also provides a data-driven approach to predict the impact of the anticipated behavior of other agents on the world without explicitly learning their policy or intentions. We demonstrate perception as prediction by learning to predict an agent's front safety and rear safety with GVFs, which encapsulate anticipation of the behavior of the vehicle in front and in the rear, respectively. The safety predictions are learned through random interactions in a simulated environment containing other agents. We show that these predictions can be used to produce similar control behavior to an LQR-based controller in an adaptive cruise control problem as well as provide advanced warning when the vehicle behind is approaching dangerously. The predictions are compact policy-based predictions that support prediction of the long term impact on safety when following a given policy. We analyze two controllers that use the learned predictions in a racing simulator to understand the value of the predictions and demonstrate their use in the realworld on a Clearpath Jackal robot and an autonomous vehicle platform.

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Architecture Design and Implementation of an Autonomous Vehicle

Cited by 58 publications

References 46 publications

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

A Survey of Autonomous Vehicles: Enabling Communication Technologies and Challenges

Fuzzy-Model-Based Output Feedback Steering Control in Autonomous Driving Subject to Actuator Constraints

Perception as prediction using general value functions in autonomous driving applications

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