A PRACTICAL STUDY ON DEMAND ANALYSIS FOR INTRODUCING LOW-SPEED AUTONOMOUS VEHICLE

2021 ◽  
Vol 76 (5) ◽  
pp. I_1093-I_1106
Author(s):  
Seishu KITAMURA ◽  
Tetsuo MIZUTA ◽  
Toshiyuki NAKAMURA ◽  
Hitomi SATO ◽  
Takayuki MORIKAWA ◽  
...  
Keyword(s):  
Autonomous Vehicle ◽  
Demand Analysis ◽  
Low Speed ◽  
On Demand

scholarly journals Optimal and Perfectly Parallel Algorithms for On-demand Data-Flow Analysis

2020 ◽  
pp. 112-140 ◽  
Author(s):  
Krishnendu Chatterjee ◽  
Amir Kafshdar Goharshady ◽  
Rasmus Ibsen-Jensen ◽  
Andreas Pavlogiannis
Keyword(s):  
Data Flow ◽  
Flow Analysis ◽  
Demand Analysis ◽  
Finite Domain ◽  
Data Flow Analysis ◽  
On Demand ◽  
Time Optimal ◽  
Speed Up ◽  
Flow Functions ◽  
Flow Graphs

AbstractInterprocedural data-flow analyses form an expressive and useful paradigm of numerous static analysis applications, such as live variables analysis, alias analysis and null pointers analysis. The most widely-used framework for interprocedural data-flow analysis is IFDS, which encompasses distributive data-flow functions over a finite domain. On-demand data-flow analyses restrict the focus of the analysis on specific program locations and data facts. This setting provides a natural split between (i) an offline (or preprocessing) phase, where the program is partially analyzed and analysis summaries are created, and (ii) an online (or query) phase, where analysis queries arrive on demand and the summaries are used to speed up answering queries.In this work, we consider on-demand IFDS analyses where the queries concern program locations of the same procedure (aka same-context queries). We exploit the fact that flow graphs of programs have low treewidth to develop faster algorithms that are space and time optimal for many common data-flow analyses, in both the preprocessing and the query phase. We also use treewidth to develop query solutions that are embarrassingly parallelizable, i.e. the total work for answering each query is split to a number of threads such that each thread performs only a constant amount of work. Finally, we implement a static analyzer based on our algorithms, and perform a series of on-demand analysis experiments on standard benchmarks. Our experimental results show a drastic speed-up of the queries after only a lightweight preprocessing phase, which significantly outperforms existing techniques.

Investigating user perception on autonomous vehicle (AV) based mobility-on-demand (MOD) services in Singapore using the logit kernel approach

2019 ◽  
Vol 46 (6) ◽  
pp. 2063-2080 ◽  
Author(s):  
Yutong Cai ◽  
Hua Wang ◽  
Ghim Ping Ong ◽  
Qiang Meng ◽  
Der-Horng Lee
Keyword(s):  
Autonomous Vehicle ◽  
User Perception ◽  
On Demand ◽  
Kernel Approach ◽  
Logit Kernel

Safety verification test study based on demand analysis

2020 ◽  
Author(s):  
Wei Yan ◽  
Leili Hu ◽  
Shuizhong Chen ◽  
Shiyong Guo ◽  
Baolin Du ◽  
...  
Keyword(s):  
Demand Analysis ◽  
Safety Verification ◽  
On Demand ◽  
Test Study ◽  
Verification Test

scholarly journals Sharing Anxiety Is in the Driver’s Seat: Analyzing User Acceptance of Dynamic Ridepooling and Its Implications for Shared Autonomous Mobility

2021 ◽  
Vol 13 (14) ◽  
pp. 7828
Author(s):  
Sigma Dolins ◽  
Helena Strömberg ◽  
Yale Z. Wong ◽  
MariAnne Karlsson
Keyword(s):  
Focus Group ◽  
New South ◽  
Autonomous Vehicle ◽  
New South Wales ◽  
Maximum Efficiency ◽  
Community Culture ◽  
On Demand ◽  
South Wales ◽  
Autonomous Mobility ◽  
The Cost

As connected, electric, and autonomous vehicle (AV) services are developed for cities, the research is conclusive that the use of these services must be shared to achieve maximum efficiency. Yet, few agencies have prioritised designing an AV system that focuses on dynamic ridepooling, and there remains a gap in the understanding of what makes people willing to share their rides. However, in 2017, the Australian transport authority Transport for New South Wales launched over a dozen trials for on-demand, shared public transport, including AVs. In this paper, we investigate the user willingness-to-share, based on experiences from one of these trials. Four focus groups (19 participants in total) were held in New South Wales with active users of either the trialled on-demand dynamic ridepooling service (Keoride) or commercial ridepooling (UberPool). Through thematic analysis of the focus group conversations, the cost, comfort, convenience, safety, community culture, and trust in authority emerged as factors that influenced the willingness-to-share. When presented with driverless scenarios, the focus group participants had significant concerns about the unknown behaviour of their co-passengers, revealing sharing anxiety as a significant barrier to the adoption of shared AVs. This paper identifies previously disregarded factors that influence the adoption of AVs and dynamic ridepooling and offers insights on how potential users’ sharing anxiety can be mitigated.

scholarly journals Public Preferences and Willingness to Pay for Shared Autonomous Vehicles Services in Nagoya, Japan

Smart Cities ◽  
2019 ◽  
Vol 2 (2) ◽  
pp. 230-244 ◽  
Author(s):  
Mingyang Hao ◽  
Yanyan Li ◽  
Toshiyuki Yamamoto
Keyword(s):  
Willingness To Pay ◽  
Autonomous Vehicles ◽  
Stated Preference ◽  
Autonomous Vehicle ◽  
Transportation Mode ◽  
Public Preferences ◽  
Socioeconomic Characteristics ◽  
On Demand ◽  
Vehicle Systems ◽  
The Relationship

Shared autonomous vehicle systems are anticipated to offer cleaner, safer, and cheaper mobility services when autonomous vehicles are finally implemented on the roads. The evaluation of people’s intentions regarding shared autonomous vehicle services appears to be critical prior to the promotion of this emerging mobility on demand approach. Based on a stated preference survey in Nagoya, Japan, the preference for shared autonomous vehicle services as well as willingness to pay for these services were examined among 1036 respondents in order to understand the relationship between people’s socioeconomic characteristics and their preferred shared autonomous vehicle services. For this purpose, k-modes clustering technique was selected and six clusters were obtained. Six groups with respect to different interests on shared autonomous vehicle services were clustered. The result of correlation analysis and discussion of willingness to pay on services provided insightful results for the future shared autonomous vehicle services. This study not only aids in revealing the demands of customer different clusters, but also states the prospective needs of users for stakeholders from research, policymaker and industry field, who are preparing to work on promoting shared autonomous vehicle systems, and subsequently, develops an optimum transportation mode by considering both demand and services as a whole.

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