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1. Sℒ 1-Simplex: Safe Velocity Regulation of Self-Driving Vehicles in Dynamic and Unforeseen Environments

   https://doi.org/10.1145/3564273  

   Mao, Yanbing; Gu, Yuliang; Hovakimyan, Naira; Sha, Lui; Voulgaris, Petros (January 2023, ACM Transactions on Cyber-Physical Systems)

   Tarek Abdelzaher, Karl-Erik Arzen (Ed.)

   This article proposes a novel extension of the Simplex architecture with model switching and model learning to achieve safe velocity regulation of self-driving vehicles in dynamic and unforeseen environments. To guarantee the reliability of autonomous vehicles, an ℒ₁adaptive controller that compensates for uncertainties and disturbances is employed by the Simplex architecture as a verified high-assurance controller (HAC) to tolerate concurrent software and physical failures. Meanwhile, the safe switching controller is incorporated into the HAC for safe velocity regulation in the dynamic (prepared) environments, through the integration of the traction control system and anti-lock braking system. Due to the high dependence of vehicle dynamics on the driving environments, the HAC leverages the finite-time model learning to timely learn and update the vehicle model for ℒ₁adaptive controller, when any deviation from the safety envelope or the uncertainty measurement threshold occurs in the unforeseen driving environments. With the integration of ℒ₁adaptive controller, safe switching controller and finite-time model learning, the vehicle’s angular and longitudinal velocities can asymptotically track the provided references in the dynamic and unforeseen driving environments, while the wheel slips are restricted to safety envelopes to prevent slipping and sliding. Finally, the effectiveness of the proposed Simplex architecture for safe velocity regulation is validated by the AutoRally platform. 

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2. Tell Me What I Need To Know: Consumers’ Desire for Information Transparency in Self-Driving Vehicles

   https://doi.org/10.1177/1071181321651240  

   Huff Jr, Earl W.; Day Grady, Siobahn; Brinnkley, Julian (September 2021, Proceedings of the Human Factors and Ergonomics Society Annual Meeting)

   Fully autonomous or “self-driving” vehicles are an emerging mobility technology with a host of potential benefits over conventional motor vehicles. Proponents argue that widespread adoption of self-driving vehicles may save countless lives and millions of dollars annually by minimizing the likelihood of deadly vehicle crashes. However, for such benefits to be realized, widespread consumer adoption of self-driving technologies is a prerequisite. Prior research suggests that while consumers are broadly optimistic about the potential of vehicle automation, there are significant concerns that may undermine consumer adoption, such as the transparency of vehicle operation. There is insufficient research into consumers’ desire to understand a self-driving vehicle’s intent and decision-making process and its impact on their willingness to adopt. We conducted a study using a 63-question internet-based survey distributed in the United States to licensed drivers 18 years of age and older (n= 996) to examine consumer preferences of the information transparency of self-driving vehicles. Our findings suggest that middle-aged and older consumers of high household income were generally optimistic about the information sharing behavior and transparency of self-driving vehicles upon availability. 

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3. Distributed Sensor Networks Deployed Using Soft Growing Robots

   https://doi.org/10.1109/RoboSoft51838.2021.9479345  

   Gruebele, Alexander M.; Zerbe, Andrew C.; Coad, Margaret M.; Okamura, Allison M.; Cutkosky, Mark R. (January 2021, International Conference on Soft Robotics (RoboSoft))

   null (Ed.)

   Due to their ability to move without sliding relative to their environment, soft growing robots are attractive for deploying distributed sensor networks in confined spaces. Sensing of the state of such robots would add to their capabilities as human-safe, adaptable manipulators. However, incorporation of distributed sensors onto soft growing robots is challenging because it requires an interface between stiff and soft materials, and the sensor network needs to undergo significant strain. In this work, we present a method for adding sensors to soft growing robots that uses flexible printed circuit boards with self-contained units of microcontrollers and sensors encased in a laminate armor that protects them from unsafe curvatures. We demonstrate the ability of this system to relay directional temperature and humidity information in hard-to-access spaces. We also demonstrate and characterize a method for sensing the growing robot shape using inertial measurement units deployed along its length, and develop a mathematical model to predict its accuracy. This work advances the capabilities of soft growing robots, as well as the field of soft robot sensing. 

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4. B-AWARE: Blockage Aware RSU Scheduling for 5G Enabled Autonomous Vehicles

   https://doi.org/10.1145/3609133  

   Szeto, Matthew; Andert, Edward; Shrivastava, Aviral; Reisslein, Martin; Lin, Chung-Wei; Richmond, Christ (October 2023, ACM Transactions on Embedded Computing Systems)

   5G Millimeter Wave (mmWave) technology holds great promise for Connected Autonomous Vehicles (CAVs) due to its ability to achieve data rates in the Gbps range. However, mmWave suffers from a high beamforming overhead and requirement of line of sight (LOS) to maintain a strong connection. For Vehicle-to-Infrastructure (V2I) scenarios, where CAVs connect to roadside units (RSUs), these drawbacks become apparent. Because vehicles are dynamic, there is a large potential for link blockages. These blockages are detrimental to the connected applications running on the vehicle, such as cooperative perception and remote driver takeover. Existing RSU selection schemes base their decisions on signal strength and vehicle trajectory alone, which is not enough to prevent the blockage of links. Many modern CAVs motion planning algorithms routinely use other vehicle’s near-future path plans, either by explicit communication among vehicles, or by prediction. In this paper, we make use of the knowledge of other vehicle’s near future path plans to further improve the RSU association mechanism for CAVs. We solve the RSU association algorithm by converting it to a shortest path problem with the objective to maximize the total communication bandwidth. We evaluate our approach, titled B-AWARE, in simulation using Simulation of Urban Mobility (SUMO) and Digital twin for self-dRiving Intelligent VEhicles (DRIVE) on 12 highway and city street scenarios with varying traffic density and RSU placements. Simulations show B-AWARE results in a 1.05× improvement of the potential datarate in the average case and 1.28× in the best case vs. the state-of-the-art. But more impressively, B-AWARE reduces the time spent with no connection by 42% in the average case and 60% in the best case as compared to the state-of-the-art methods. This is a result of B-AWARE reducing nearly 100% of blockage occurrences. 

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5. What Can My Car Tell Me? Consumer Perceptions of Transparency in Self-Driving Vehicles

   https://doi.org/10.1109/ICHMS56717.2022.9980645  

   Huff, Earl W.; Tucker, Natalie; Grady, Siobahn Day; Brinkley, Julian (November 2022, 2022 IEEE 3rd International Conference on Human-Machine Systems (ICHMS))

   Fully autonomous or “self-driving” vehicles are emerging mobility technology with potential benefits over conventional motor vehicles. Proponents argue that the widespread adoption of autonomous vehicles may save countless lives and millions of dollars annually by minimizing the likelihood of deadly vehicle crashes. However, widespread adoption of automated-driving technologies is required to realize such benefits, which research suggests, may be undermined by consumer concerns about vehicle operation transparency. Further, there is insufficient research into consumer perceptions of an autonomous vehicle’s communication and information-sharing behavior, which may impact their likelihood of purchasing one. We conducted a study using a 63-question internet-based survey distributed in the United States to licensed drivers 18 years of age and older (n=996) to examine consumer perceptions of autonomous vehicles across accountability, communication, information sharing, and concerns. Our findings show that consumer perceptions of the four dimensions vary significantly across race, gender, and ability. 

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