Impressions after an automated mobility experience: An acceptance study

Pedestrians' perceptions, fixations, and decisions towards automated vehicles with varied appearances.

In this study, we analyzed mobility patterns of at-risk populations affected by an extreme event using geotagged tweets to geo-target power restoration efforts. Unlike other studies that have used tweets to facilitate emergency management activities, we used 1.5 million geotagged tweets generated during Hurricane Sandy (2012) to determine the mobility patterns and geospatial distribution of impacted populations who experienced power outage before, during and after the hurricane. We implemented a three-step analytical framework to: (i) analyze tweet contents with visual methods, including dendrograms, word clouds to identify common keywords pertaining to power outage; (ii) identify target users whose tweets contained information about power outages; and (iii) create a user-tweet locations matrix and an origin-destination matrix to examine clusters of target users and their mobility patterns. Preliminary results indicate that potential clusters were present in and around New York city, Philadelphia, Washington D.C. and Baltimore, which were used as potential evacuation destination cities after hurricane Sandy. The travel pattern and destination information can be used to (i) mobilize restoration efforts by utility companies and (ii) address resource allocation needs both in impacted and destination cities. Future work will focus on analyzing potential destinations for different origins and travel-time to identify evacuation routing patterns.

A joint behavioral choice model for adoption of automated vehicle ride sourcing and carsharing technologies: Role of built environment & sustainable travel behaviors

The recent and dramatic growth in ride-hailing activity is a bellwether of a coming transportation revolution driven by on-demand services. The impacts of ride-hailing services on the transportation system have been immediate and major. Yet, public agencies are only beginning to understand their magnitude because the private ride-hailing industry has provided limited amounts of meaningful data. Consequently, public agencies responsible for managing congestion and providing transit services are unable to clearly determine who uses ride-hailing services and how their adoption influences established travel modes, or forecast the potential growth of this emergent mode in the future. To address these pressing questions, an intercept survey of ride-hailing passengers was conducted in the Greater Boston region in fall 2017. Ten ride-hailing drivers, recruited and trained by the authors, asked passengers to complete surveys during their ride-hailing trip. The tablet-based survey instrument recorded nearly 1,000 passenger responses with regard to socioeconomic background, mobility options, and trip context. These responses, which enabled a robust description of ride-hailing passengers for the region, were used to analyze how new on-demand mobility services such as Uber and Lyft may be substituting travel by other modes. The study substantiates previous findings and advances knowledge of who is utilizing this new mobility option and what factors influence its adoption over public and active transportation modes. The results are intended to inform public policies ensuring that shared mobility technologies will complement existing multimodal landscapes and not worsen existing environmental concerns or equity gaps related to individual mobility.

Autonomous vehicle (AV) technologies are under constant improvement with pilot programs now underway in several urban areas worldwide. Modeling and field-testing efforts are demonstrating that shared mobility coupled with AV technology for automated mobility on-demand (AMoD) service may significantly impact levels of service and environmental outcomes in future cities. Given these rapidly emerging developments, there is an urgent need for methods to adequately quantify the economic impacts of new vehicle technologies and future urban mobility policy. In this paper, we show how broader user-centric impacts can be captured by the activity-based accessibility (ABA) measure, which takes advantage of the rich data and outcomes of utility-maximization activity-based models and its interaction with mesoscale agent-based traffic simulation frameworks. Using the SimMobility simulator, we evaluate shared AMoD strategies applied to a Singapore micromodel city testbed. A near-future strategy of exclusive availability of AMoD service in the central business district (CBD), and a further-horizon strategy of the full operation of AMoD city-wide in the absence of other on-demand services, were tested and evaluated. Our results provide insights into the income and accessibility effects on the population under the implementation of shared and automated mobility policies. The outcomes indicate that the city-wide deployment of AMoD results in greater accessibility and network performance. Moreover, the accessibility of low-income individuals is improved relative to that of mid- and high-income individuals. The restriction of AMoD to the CBD along with the operation of other on-demand services, however, provides a certain level of disbenefit to segments of the population in two exceptional cases. The first is to high-income individuals who live in a suburban zone and rely heavily on on-demand services; the second is to mid-income residents that have excellent public transportation coverage with close proximity to the CBD. We further establish the efficacy of the ABA measure, as these findings motivate the need for measuring socioeconomic impacts at the individual level. The work presented here serves as a foundation for policy evaluation in real-world urban models for future mobility paradigms.

In line with the trend of global transport volume which has increased rapidly over the years, internal transportation in seaports is always conducted with high frequency. Thus, there is always much potential for traffic jams as well as high transportation costs and emissions. Many efforts have been initiated to streamline the inter-terminal container transport (ITT) through the development of automated vehicles and equipment as well as using private transport facilities to overcome these limitations. The purpose of this paper to develop a framework to design, analyze, and validate the efficiency of a new ITT system in a port area based on the monorail network and automatic vehicles. First, the number of shuttles and loaders was determined depending on the transport demand scenario. Next, a simulation model was applied to evaluate the system performance as well as gain more insight into the working process of the ITT system. Finally, by setting goals for the performance indicators, the results showed that the system was highly efficient with 100% of the containers delivered to their destination on time. Besides, a series of other performance tracking was provided to provide insight into the system's capabilities.

Investigation on steady state performance of hydrodynamic automatic transmission vehicle

<div class="section abstract"><div class="htmlview paragraph">With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink software is established, providing a comprehensive environment for simulating AV behavior. Secondly, an evaluation system is developed to assess AV’s feasibility on longitudinal roads, based on safety indicators (rear-end collisions occurrence) and comfort indicators (axial acceleration, vertical acceleration, and vertical acceleration change rate). Lastly, the feasibility of ACC-AVs on existing longitudinal profile roads is simulated and evaluated. The results indicate that, on straight slope section, ACC-AVs may experience rear-end collisions on downhill sections with design speeds below 100 km/h; when safety requirements are met, both uphill and downhill sections exhibit good comfort levels. For the vertical curve section, comfort is also favorable in segments with low design speeds and large vertical curve radii, however, as curve radii decrease or design speeds increase, comfort deteriorates. The findings of this study provide a reference for optimizing highway profile design for AVs.</div></div>

<div class="section abstract"><div class="htmlview paragraph">The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety.</div><div class="htmlview paragraph"><b>Human-like Decision-making and Control for Automated Driving </b>takes a step forward to address breaking through the limitation of future automation applications, investigating in depth:</div><div class="htmlview paragraph"><ul class="list disc"><li class="list-item"><div class="htmlview paragraph">Human driving feature modeling and analysis</div></li><li class="list-item"><div class="htmlview paragraph">Personalized motion control for AVs</div></li><li class="list-item"><div class="htmlview paragraph">Human-like decision making for AVs</div></li></ul></div><div class="htmlview paragraph"><a href="https://www.sae.org/publications/edge-research-reports" target="_blank">Click here to access the full SAE EDGE</a><sup>TM</sup><a href="https://www.sae.org/publications/edge-research-reports" target="_blank"> Research Report portfolio.</a></div></div>

Advances in Detector Systems for Imaging Single Molecule Fluorescence

The aim of this paper is to investigate new neural network algorithms, architectures and models which are inspired by the image processing capability of the visual system in the human brain, and subsequently to apply these models to medical imaging(MI) applications. Realizing the neural network architecture on FPGA that supports parallelism and reconfigurability. A sincere attempt is taken up to implement the complex and massive parallel architecture on FPGA optimising speed, area, and power with out compromising on image quality and SNR that is a very important criteria for medical image compression. Multi layered neural network (NN) architecture is proposed for compression of high-resolution image, the architecture is implemented on FPGA as it supports reconfigurability. The architecture considered has N-M-N (64-4-64) multi-layered NN structure, which achieves compression ratio of (CR) 93.75. Compression ratio is reconfigurable with change in M. The architecture is generalized and can achieve compression ratios from 2 to 99, which is reconfigurable. Performance of any neural network architecture for compression depends on training; this architecture considers general back propagation training. The training is performed offline, with known set of image samples consisting most of the properties of any standard image. The Mean Square Error (MSE) computed during every iteration of training is scaled and fed back into the network to update the weight matrix at specific points, this reduces the training time. As the weight matrix occupies more space for storage, the redundancies in the weight matrix are exploited and a storage space is created with minimum memory requirement on FPGA. Compression ratios obtained demonstrate performance superiority of the network as compared with JPEG compression standard. Inserting noise on the compressed sets of data, tests the network performance. The hardware complexity, area requirement, speed is compared and discussed; there is a saving of 22% of space on FPGA, with 40% increase in speed, and reducing the power by 12%. The major advantage of this architecture is its reconfigurability on the architecture size that achieves different compression ratios.

High-capacity Connected and Autonomous Vehicles (CAVs) are expected to be extensively utilized by on-demand services. This paper aims to assess the impacts of large-scale autonomous on-demand mobility services on traffic, environment, and road safety, under various service specifications using microsimulation. To that end, an urban on-demand shuttle service was designed, optimized, based on a variation of the Dial-a-Ride optimization problem (DARP), and implemented in the road network of the city of Athens to serve different portions of demand with various capacity specifications. It was then investigated through forty mobility scenarios, with differences in policy implementation and market penetration rate of CAVs. Findings show that it led to improved network level traffic conditions, as delays decreased, and that traffic impacts evolve with fleet capacity and served demand. Furthermore, the number of conflicts decreased and the environmental conditions significantly improved, with CAVs in the network, while the traveled distance increased.

Problem statement: In any real time biometric system processing speed and recognition time are crucial parameters. Reducing processing time involves many parameters like normalization, FAR, FRR, management of eyelid and eyelash occlusions, size of signature etc. Normalization consumes substantial amount of time of the system. This study contributes for improved iris recognition system with reduced processing time, False Acceptance Rate (FAR) and False Rejection Rate (FRR). Approach: To improve system performance and reliability of a biometric system. It avoided the iris normalization process used traditionally in iris recognition systems. The technique proposed here used different masks to filter out iris image from an eye. Comparative study of different masks was done and optimized mask is proposed. The experiment was carried on CASIA database consisting of 756 iris images of 108 persons. Each person contributes seven images of eye (108×7 = 756) images in the database. Results: In the proposed method: (1) Normalization step is avoided; (2) Computational time is reduced by 0.3342 sec; (3) Iris signature size is reduced; (4) Improved performance parameters. (With reduced feature size, proposed method achieves 99.4866% accuracy, 0.0069% FAR, 1.0198% FRR and significant increase in speed of the system). Conclusion: Iris signature proposed was comparatively small just of 1×24 size. Though Daugman’s method gives best accuracy of 99.90% but the iris signature length used by that algorithm is comparatively very high that is 1×2048 phase vector. Also Daugman has used phase information in signature formation. Our method gives a accuracy of 99.474% with a signature of comparatively very small length. This has definitely contributed to improve the speed.

Dynamic braking performance is well understood in conventional vehicles, but its characteristics in ultra-lightweight, energy-efficient prototype vehicles remain poorly documented, despite their growing role in energy efficiency competitions and urban mobility concepts. This study experimentally investigated stop braking performance under varying vehicle speeds and masses in a low-mass prototype vehicle (V1.0). Tests were conducted at four speed levels (10, 20, 30, and 40 km/h) and two mass configurations (135 kg and 165 kg), with each scenario repeated five times on a flat 100 m track. With an increase in speed from 30 to 40 km/h, the 135-kg configuration showed increases in braking distance of 11.14 m (133%), braking time of 1.92 s (47%), and disc pad temperature of 0.66 °C (1.5%). The 165-kg configuration showed corresponding increases of 14.21 m (152%), 3.02 s (75%), and 1.40 °C (3.3%). Across the full test range, increasing speed from 10 km/h to 40 km/h for the 135-kg configuration increased braking distance from 1.78 m to 19.5 m (+995%) and braking time from 1.63 s to 5.04 s (+209%). Increasing mass from 135 kg to 165 kg at 30 km/h increased braking distance by 0.97 m (11.6%) and braking time by 0.59 s (17.3%). Disc pad temperatures remained within a safe range, rising only from 41.1 °C to 42.5 °C (+3.4%) across the tested speeds. These quantitative findings provide critical data for optimizing braking system design in lightweight, energy-efficient prototypes, ensuring operational safety under various load and speed conditions.

Vast coastal water bodies such as mangroves and wetlands, despite their enormous importance, are inaccessible to even the smallest aquatic vehicles, which are not capable of navigating the extremely shallow environments. To understand their behavior and the transport and exchange processes with both sea and land, it is crucial to study local hydrodynamics, which are highly dependent on bathymetric configuration. In order to obtain accurate and high spatial resolution bathymetric samples, an existing small surface vehicle instrumented with GPS and echosounder was implemented with an autopilot system to achieve autonomy. A description of the autonomous system together with the evaluation of the whole vehicle's performance in a natural coastal environment (e.g., affected by the wind, waves, and currents that exist in coastal regions) is presented. The navigation system is based on an autopilot system, which follows a set of user-selected waypoints to drive the vehicle through a previously designed path. The trajectory is self-corrected to achieve sufficient accuracy (the limits are also defined by the user) using real-time kinematics. The tracking is performed by two independent proportional-integral-derivative (PID) control systems: the boat's displacement speed and the bearing control. A simple experimental procedure to tune the low-level PID control parameters is presented. Comparisons between the bathymetric maps obtained with the system and with other standard bathymetric surveys from a selected coastal site (ship-towed echosounder) are presented to test both the precision of the boat's navigation and the fine structure of the bottom topography. The results confirm the system's capability as a valuable tool for surveying large and very shallow environments.