scholarly journals Visualising User Experiences: Analysing Embodiment of UX in Autonomous Vehicle Concepts

2019 ◽  
Vol 1 (1) ◽  
pp. 4039-4048
Author(s):  
Charlie Ranscombe ◽  
Jacob Rodda ◽  
Mark Johnson
Keyword(s):  
User Experience ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Asia Pacific ◽  
Vehicle Interior ◽  
User Experiences ◽  
Contextual Features ◽  
Vehicle Operation ◽  
Starting Point

AbstractThe prospect of autonomous vehicles and associated technologies has disrupted traditional modes of vehicle operation and ownership. This requires automotive designers to shift their focus from designing vehicle form to consider the design of transport experiences. As such, there is a need to explore how best to support automotive designers in communicating user experiences (UX) alongside the physical design of vehicles. This paper presents an industry case study conducted with Ford Design Asia Pacific to assess the embodiment of UX in early concepts. Attributes of generalised model for UX are mapped to designers' storyboard illustration for the experience of an advanced concept for an autonomous vehicle interior. Results show how a mix of captions, sketches of users and contextual features illustrate different attributes of user experience. From findings we conclude firstly, the need to develop a toolkit to help designers communicate descriptions of as yet designed interactions. We also conclude that sketching contextual features of experience can provide a starting point to develop aspects of UX that can be used to differentiate and identify the Ford brand.

Integration of GPS and Accelerometer Uncertainties to Improve the Estimation of the Pose of Autonomous Vehicles

2010 ◽  
Author(s):  
Wilson O. Achicanoy M. ◽  
Carlos F. Rodriguez H.
Keyword(s):  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Sampling Rate ◽  
System A ◽  
Accelerometer Signal ◽  
Gps Measurement ◽  
Global Positioning ◽  
The Individual ◽  
Uncertainty Models

Uncertainty fusion techniques based on Kalman filtering are commonly used to provide a better estimation of the state of a system. A comparison between three different methods to combine the sensor information in order to improve the estimation of the pose of an autonomous vehicle is presented. Two sensors and their uncertainty models are used to measure the observables states of a process: a Global Positioning System (GPS) and an accelerometer. Given that GPS has low sampling rate and the uncertainty of the position, calculated by double integration from the accelerometer signal, increases with time, first a resetting of the estimator based on accelerometer by the GPS measurement is done. Next, a second method makes the fusion of both sensor uncertainties to calculate the estimation. Finally, a double estimation is done, one for each sensor, and a estimated state is calculated joining the individual estimations. These methods are explained by a case study of a guided bomb.

scholarly journals Clothoid: An Integrated Hierarchical Framework for Autonomous Driving in a Dynamic Urban Environment

Sensors ◽  
2020 ◽  
Vol 20 (18) ◽  
pp. 5053 ◽  
Author(s):  
Saba Arshad ◽  
Muhammad Sualeh ◽  
Dohyeong Kim ◽  
Dinh Van Nam ◽  
Gon-Woo Kim
Keyword(s):  
Path Planning ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Autonomous Driving ◽  
Unified Framework ◽  
Human Errors ◽  
Safe Driving ◽  
Planning And Control ◽  
Starting Point ◽  
And Control

In recent years, research and development of autonomous driving technology have gained much interest. Many autonomous driving frameworks have been developed in the past. However, building a safely operating fully functional autonomous driving framework is still a challenge. Several accidents have been occurred with autonomous vehicles, including Tesla and Volvo XC90, resulting in serious personal injuries and death. One of the major reasons is the increase in urbanization and mobility demands. The autonomous vehicle is expected to increase road safety while reducing road accidents that occur due to human errors. The accurate sensing of the environment and safe driving under various scenarios must be ensured to achieve the highest level of autonomy. This research presents Clothoid, a unified framework for fully autonomous vehicles, that integrates the modules of HD mapping, localization, environmental perception, path planning, and control while considering the safety, comfort, and scalability in the real traffic environment. The proposed framework enables obstacle avoidance, pedestrian safety, object detection, road blockage avoidance, path planning for single-lane and multi-lane routes, and safe driving of vehicles throughout the journey. The performance of each module has been validated in K-City under multiple scenarios where Clothoid has been driven safely from the starting point to the goal point. The vehicle was one of the top five to successfully finish the autonomous vehicle challenge (AVC) in the Hyundai AVC.

scholarly journals A Framework for Component Selection Considering Dark Sides of Artificial Intelligence: A Case Study on Autonomous Vehicle

Electronics ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 384
Author(s):  
Mohammad Reza Jabbarpour ◽  
Ali Mohammad Saghiri ◽  
Mehdi Sookhak
Keyword(s):  
Artificial Intelligence ◽  
Autonomous Vehicles ◽  
Intelligent Systems ◽  
Selection Process ◽  
Smart Cities ◽  
Autonomous Vehicle ◽  
Component Selection ◽  
Wide Range ◽  
Selection Framework

Nowadays, intelligent systems play an important role in a wide range of applications, including financial ones, smart cities, healthcare, and transportation. Most of the intelligent systems are composed of prefabricated components. Inappropriate composition of components may lead to unsafe, power-consuming, and vulnerable intelligent systems. Although artificial intelligence-based systems can provide various advantages for humanity, they have several dark sides that can affect our lives. Some terms, such as security, trust, privacy, safety, and fairness, relate to the dark sides of artificial intelligence, which may be inherent to the intelligent systems. Existing solutions either focus on solving a specific problem or consider the some other challenge without addressing the fundamental issues of artificial intelligence. In other words, there is no general framework to conduct a component selection process while considering the dark sides in the literature. Hence, in this paper, we proposed a new framework for the component selection of intelligent systems while considering the dark sides of artificial intelligence. This framework consists of four phases, namely, component analyzing, extracting criteria and weighting, formulating the problem as multiple knapsacks, and finding components. To the best of our knowledge, this is the first component selection framework to deal with the dark sides of artificial intelligence. We also developed a case study for the component selection issue in autonomous vehicles to demonstrate the application of the proposed framework. Six components along with four criteria (i.e., energy consumption, security, privacy, and complexity) were analyzed and weighted by experts via analytic hierarchy process (AHP) method. The results clearly show that the appropriate composition of components was selected through the proposed framework for the desired functions.

scholarly journals USER NEED-ORIENTED CONCEPT DEVELOPMENT OF AUTONOMOUS VEHICLES

2021 ◽  
Vol 1 ◽  
pp. 3349-3358
Author(s):  
Ferdinand Schockenhoff ◽  
Adrian König ◽  
Maximilian Zähringer ◽  
Markus Lienkamp
Keyword(s):  
21St Century ◽  
Autonomous Vehicles ◽  
Development Process ◽  
Autonomous Vehicle ◽  
Concept Development ◽  
Vehicle Concept ◽  
Design Concepts ◽  
Starting Point ◽  
User Need ◽  
Input Variables

AbstractVehicle concept development is a domain that has applied and detailed its process over decades. The megatrends of the 21st century of “automation” and “sharing” influence the vehicle concept in such a manner that this well-running process needs an update. The vehicle itself and the customer of the vehicle are changing and therefore the components of the vehicle and the input variables of the useroriented design of the vehicle concept must be changed as well. We present a development process for autonomous vehicle concepts to address these challenges. We are therefore analyzing the current definition of a vehicle concept and its development process. Based on a literature review of a selection of common design methodologies, we update this definition for autonomous vehicle concepts and present a development process that presents design concepts of autonomous vehicle in a user need oriented way. This includes the sharing of models since user needs could be fulfilled by more than one vehicle concept. We believe that the presented process can be a starting point for vehicle concept development of the 21st century.

Lights, Camera, Autonomy! Exploring the Opinions of Older Adults Regarding Autonomous Vehicles Through Enactment

2020 ◽  
Vol 64 (1) ◽  
pp. 1971-1975
Author(s):  
Aaron Gluck ◽  
Earl W. Huff ◽  
Mengyuan Zhang ◽  
Julian Brinkley
Keyword(s):  
Older Adults ◽  
Pilot Study ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Design Considerations ◽  
The Past ◽  
Operational Risks ◽  
Vehicle Operation ◽  
Growing Population

Autonomous vehicles (AV), one of the transportation industry’s biggest innovations of the past few decades, bring the promise of safer roads and significantly lower vehicle-related fatalities. While many studies have found largely positive consumer opinions regarding operating and owning such a vehicle, older adults (55+) tend to express concerns about the safety and operational risks of a vehicle with unknown capabilities. To investigate how older adults and AVs may interact, we conducted an improv-style enactment-based partic-ipatory design pilot study. We found that initial concerns about trust and safety can be diminished through training and repetitive successful vehicle operation. Additionally, our participants provided insights into the AV design considerations, needs, and interactions for older adults. These findings add to the collective body of autonomous vehicle research by demonstrating that the needs of this growing population, who may benefit significantly from access to AVs, should be considered by manufacturers.

FPGA Based Vehicle to Vehicle Communication in Spartan 3E

2020 ◽  
Vol 8 ◽  
pp. 14-21
Author(s):  
Surya Man Koju ◽  
Nikil Thapa
Keyword(s):  
Signal Processing ◽  
Autonomous Vehicle ◽  
Processing Unit ◽  
Warning Signals ◽  
Vehicle Communication ◽  
Digital Vlsi ◽  
Vehicle To Vehicle ◽  
Vehicle Operation ◽  
Vehicle Technology ◽  
The Voice

This paper presents economic and reconfigurable RF based wireless communication at 2.4 GHz between two vehicles. It implements digital VLSI using two Spartan 3E FPGAs, where one vehicle receives the information of another vehicle and shares its own information to another vehicle. The information includes vehicle’s speed, location, heading and its operation, such as braking status and turning status. It implements autonomous vehicle technology. In this work, FPGA is used as central signal processing unit which is interfaced with two microcontrollers (ATmega328P). Microcontroller-1 is interfaced with compass module, GPS module, DF Player mini and nRF24L01 module. This microcontroller determines the relative position and the relative heading as seen from one vehicle to another. Microcontroller-2 is used to measure the speed of vehicle digitally. The resulting data from these microcontrollers are transmitted separately and serially through UART interface to FPGA. At FPGA, different signal processing such as speed comparison, turn comparison, distance range measurement and vehicle operation processing, are carried out to generate the voice announcement command, warning signals, event signals, and such outputs are utilized to warn drivers about potential accidents and prevent crashes before event happens.

Heineken's Acquisition of Asia Pacific Breweries: Accounting for Business Combinations and Changes in Ownership Interests

2017 ◽  
Vol 32 (4) ◽  
pp. 101-127 ◽  
Author(s):  
Pearl Tan ◽  
Chu-Yeong Lim
Keyword(s):  
Market Share ◽  
Joint Venture ◽  
Interesting Case ◽  
Asia Pacific ◽  
Joint Control ◽  
Business Combinations ◽  
Final Offer ◽  
Control Premium ◽  
Joint Venture Partner

ABSTRACT On July 20, 2012, Heineken, a Dutch brewery offered S$5.125 billion (Singapore dollars; approximately US$4.1 billion) to buy Asia Pacific Breweries Ltd (APB; formerly, Malayan Breweries Limited) from its Singapore-based joint venture partner, Fraser and Neave, Limited. (F&N). At that point, Heineken and F&N had joint control over APB through the joint venture vehicle Asia Pacific Investments Pte Ltd (APIPL). Brewery business under the joint arrangement had moved on quite predictably from the time APB was formed in 1931. However, the calm changed to high drama when Thai Beverage, owned by one of Thailand's tycoons, made a bid for F&N and APB. Heineken was quick to respond by aggressively buying shares of APB, leading to a large control premium being paid in the final offer price. The bidding war was largely motivated by the Dutch and Thai beer giants, each wanting to own the iconic Tiger beer brand that was owned by APB and thus take control of APB's strong market share in the fast-growing market of Asia. The Heineken bid for APB presents an interesting case study regarding the motivations for acquisitions, the nature of control, and accounting for acquisitions. The case also presents rich issues in accounting for changes in ownership interests with and without gain of control.

Vision-Based Navigation of Autonomous Vehicles in Roadway Environments with Unexpected Hazards

2019 ◽  
Vol 2673 (12) ◽  
pp. 494-507 ◽  
Author(s):  
Mhafuzul Islam ◽  
Mashrur Chowdhury ◽  
Hongda Li ◽  
Hongxin Hu
Keyword(s):  
Object Detection ◽  
Autonomous Vehicles ◽  
Autonomous Vehicle ◽  
Semantic Segmentation ◽  
Steering Wheel ◽  
Potential Hazard ◽  
Driving System ◽  
Hazardous Object ◽  
Vision Based Navigation ◽  
Navigational System

Vision-based navigation of autonomous vehicles primarily depends on the deep neural network (DNN) based systems in which the controller obtains input from sensors/detectors, such as cameras, and produces a vehicle control output, such as a steering wheel angle to navigate the vehicle safely in a roadway traffic environment. Typically, these DNN-based systems in the autonomous vehicle are trained through supervised learning; however, recent studies show that a trained DNN-based system can be compromised by perturbation or adverse inputs. Similarly, this perturbation can be introduced into the DNN-based systems of autonomous vehicles by unexpected roadway hazards, such as debris or roadblocks. In this study, we first introduce a hazardous roadway environment that can compromise the DNN-based navigational system of an autonomous vehicle, and produce an incorrect steering wheel angle, which could cause crashes resulting in fatality or injury. Then, we develop a DNN-based autonomous vehicle driving system using object detection and semantic segmentation to mitigate the adverse effect of this type of hazard, which helps the autonomous vehicle to navigate safely around such hazards. We find that our developed DNN-based autonomous vehicle driving system, including hazardous object detection and semantic segmentation, improves the navigational ability of an autonomous vehicle to avoid a potential hazard by 21% compared with the traditional DNN-based autonomous vehicle driving system.

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