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Abstract Lane changes are stressful maneuvers for drivers, particularly during high-speed traffic flows. However, modeling driver’s lane-changing decision and implementation process is challenging due to the complexity and uncertainty of driving behaviors. To address this issue, this article presents a personalized Lane-Changing Model (LCM) for Advanced Driver Assistance System (ADAS) based on deep learning method. The LCM contains three major computational components. Firstly, with abundant inputs of Root Residual Network (Root-ResNet), LCM is able to exploit more local information from the front view video data. Secondly, the LCM has an ability of learning the global spatial-temporal information via Temporal Modeling Blocks (TMBs). Finally, a two-layer Long Short-Term Memory (LSTM) network is used to learn video contextual features combined with lane boundary based distance features in lane change events.

Abstract Introduction: This study evaluates driving ability in those with Charcot Marie Tooth Disease Type 1A, a hereditary peripheral neuropathy. Methods: Individuals with Charcot Marie Tooth Disease Type 1A (n = 18, age = 42 ± 7) and controls (n = 19; age = 35 ± 10) were evaluated in a driving simulator. The Charcot Marie Tooth Neuropathy Score version 2 was obtained for individuals. Rank Sum test and Spearman rank correlations were used for statistical analysis. Results: A 74% higher rate of lane departures and an 89% higher rate of lane deviations were seen in those with Charcot Marie Tooth Disease Type 1A than for controls (p = 0.005 and p < 0.001, respectively). Lane control variability was 10% higher for the individual group and correlated with the neuropathy score (rS = 0.518, p = 0.040), specifically sensory loss (rS = 0.710, p = 0.002) and pinprick sensation loss in the leg (rS = 0.490, p = 0.054).

Abstract Real-time reconstruction of 3D environment attributed with semantic information is significant for a variety of applications, such as obstacle detection, traffic scene comprehension and autonomous navigation. The current approaches to achieve it are mainly using stereo vision, Structure from Motion (SfM) or mobile LiDAR sensors. Each of these approaches has its own limitation, stereo vision has high computational cost, SfM needs accurate calibration between a sequences of images, and the onboard LiDAR sensor can only provide sparse points without color information. This paper describes a novel method for traffic scene semantic segmentation by combining sparse LiDAR point cloud (e.g. from Velodyne scans), with monocular color image. The key novelty of the method is the semantic coupling of stereoscopic point cloud with color lattice from camera image labelled through a Convolutional Neural Network (CNN).

Abstract A two-dimensional model of three elements, high-lift airfoil, was designed at a Reynolds number of ?????? using computational fluid dynamics (CFD) to generate downforce with good lift-to-drag efficiency for a formula student open-wheel race car basing on the nominal track speeds. The numerical solver uses the Reynolds-averaged Navier-Stokes (RANS) equation model coupled with the Langtry-Menter four-equation transition shear stress transport (SST) turbulence model. Such model adds two further equations to the ?? − ?? SST model resulting in an accurate prediction for the amount of flow separation due to adverse pressure gradient in low Reynolds number flow. The ?? − ?? SST model includes the transport effects into the eddy-viscosity formulation, whereas the two equations of transition momentum thickness Reynolds number and intermittency should further consider transition effects at low Reynolds number.

Abstract Today’s explosive condition of the market is compelling the manufacturing organizations to switch from traditional manufacturing (TM) to lean manufacturing (LM) to create a footprint in this competitive era. In this article, 16 critical success factors (CSFs) for LM implementation are identified through a vast literature review, the opinion of academicians and industry experts and interpretive structural modeling (ISM) is used to create interrelationships among the identified CSFs, and interpretive ranking process (IRP) rank these CSFs based on dominance with respect to performance dimensions. Leadership and management made the foundation of an ISM model while the training and people development have secured the first rank in the IRP model. Implementation of such ISM- and IRP-based models of CSF would give a clear understanding of these CSFs so that LM researchers, decision-makers, managers, and practitioners of LM will use their resources more efficiently.

Abstract In an automated machining process, monitoring the conditions of the tool is essential for deciding to replace or repair the tool without any manual intervention. Intelligent models built with sensor information and machine learning techniques are predicting the condition of the tool with good accuracy. In this study, statistical models are developed to identify the conditions of the abrasive grinding wheel using the Acoustic Emission (AE) signature acquired during the surface grinding operation. Abrasive grinding wheel conditions are identified using the abrasive wheel wear plot established by conducting experiments. The piezoelectric sensor is used to capture the AE from the grinding process, and statistical features of the abrasive wheel conditions are extracted in time and wavelet domains of the signature. Machine learning algorithms, namely, Classification and Regression Trees (CART) and Support Vector Classifiers (SVC), are used to build statistical models.

Abstract Management of expressway networks has been mainly focused on defect management without looking at the correlations with accidental risks. This causes unsustainability in expressway infrastructure maintenance since such defects may not be a contributing factor toward public safety. Thus it is necessary to incorporate accidental events for decision-making in infrastructure management. This study has developed a novel approach to machine learning (ML) that incorporates actual primary data from the last 10 years of single-vehicle accidents (SVA) by collisions with motorway facilities, or so-called single-vehicle collisions with fixed objects. The ML is firstly aimed at identifying the influential factors of SVA in relation to finding effective countermeasures for accidents by integrating the correlation analysis, multiple regression analysis, and ML techniques. The study reveals that wet pavement conditions have a significant effect on SVA.

Abstract The ability of senior citizens as well as other members of the general population to engage in an effective manner with technology is of increasing importance as new and innovative technologies become available. While recognizing the challenges that technologies can have on different populations, the ability to interact successfully with new technologies will, for seniors, have important consequences that can affect their quality of life and those of their families in numerous and important ways. This study, building upon previous research, examines the major dimensions of decision-making regarding attitudes toward autonomous vehicle technologies (ATVs) and their use. The study utilized data from a study of senior citizens in the Dallas-Fort Worth (DFW) area and compared the results with a sample of graduate students from a local university.

2020-2021 Reviewers

Abstract A new method that allows data-enabled (empirical) models, commonly used for automotive engine calibration, to extrapolate beyond the range of training data has been developed. This method used a physics-based system-level one-dimensional model to improve interpolation and allow extrapolation for three data-based algorithms, by modifying the model input (feature) space. Neural network, regression, and k-nearest neighbor predictions of engine emissions and volumetric efficiency were greatly improved by generating 736,281 artificial feature spaces and then performing feature selection to choose feature spaces (feature selection) so that extrapolations in the original feature space were interpolations in the new feature space. A novel feature selection method was developed that used a two-stage search process to uniquely select the best feature spaces for every prediction.

Abstract In recent years, to avoid the failure of the onboard perception system, intelligent vehicle infrastructure cooperative systems have been attracting attention in the field of autonomous vehicles. Using the perception technology of roadside sensors to provide supplementary traffic information for autonomous vehicles has become an increasing trend. Several roadside perception solutions select deep learning for three-dimensional (3D) object detection. However, deep learning methods have several issues and lack reliability in practical engineering applications. To tackle this challenge, this study proposes a pedestrian detection algorithm based on roadside Light Detection And Ranging (LiDAR) by combining traditional and deep learning algorithms. To meet real-time demand, Octree with region-of-interest (ROI) selection is introduced and improved to filter the background in each frame, which improves the clustering speed.

Abstract Identifying and eliminating failure scenarios is critical in the development of autonomous vehicle (AV) systems. However, finding such failures through real-world vehicle-level testing is a difficult task as system disengagements and accidents are rare occurrences. Simulation approaches have been proposed to supplement vehicle-level testing and reduce the costs associated with operating large fleets of autonomous test vehicles. While one can run more vehicles in simulation than in the real world, applying traditional Monte Carlo sampling techniques to find failures still yields an unguided search and a large waste of computing resources. A more directed method than random sampling is needed to identify failure scenarios in a computationally efficient manner. Adaptive Stress Testing (AST) is a method that uses reinforcement learning (RL) paradigms to efficiently find failure scenarios in stochastic sequential decision-making systems.

Abstract This article describes the application of Reinforcement Learning (RL) with an embedded heuristic algorithm to a multi-objective hybrid vehicle optimization. A multi-objective optimization problem (MOP) is defined as a minimization of total energy consumption and trip time resulting from optimal control of vehicle speed over a known route. First, a computationally efficient heuristic optimization algorithm is formulated to solve the MOP for multiple traffic scenarios. Then, the off-line integration of RL is applied to the heuristic optimization algorithm process and utilized to solve the MOP. Finally, the online optimization capability of the machine learning algorithm is discussed, as well as its extension to the vehicle routing problem and the hybrid electric vehicle. The specific scenario investigated is where a generic vehicle begins a trip on a one-lane highway. The length of the highway and the number of vehicles and traffic signals on the road are generic as well.

Abstract It is a challenge to find a safe trajectory for automated vehicles in urban environments with pedestrians. The prediction of future movements with 100% certainty is impossible, if the intention is unknown. A Gaussian process approach is used to formulate future movement hypotheses of the pedestrian based on historical movements. A mixed integer linear programming (MILP) optimization approach is used for the trajectory planning of the vehicle. The collision probability between the ego-vehicle and pedestrian is used as constraints in the optimization. This approach is useful for cooperative vehicle systems, with historical movement data in a fixed urban environment (e.g., intersection) and the premise that pedestrians follow typical movement data.

Abstract This work was supported jointly by the National Science Foundation of China under Grant No. 51875184 and the National key R&D programs, China New energy vehicles focus on special projects under Grant No. 2016YFB0100903-2.

Abstract Car manufacturers are continuously improving passenger comfort by advancing technologies including highly automated driving. Before the broad introduction of automated driving, specific human factors regarding passenger comfort must be considered, including motion sickness. Therefore, the identification of the frequency of motion sickness and associated factors in the population is needed to extrapolate the effects for future mobility systems. We conducted three surveys between 2015 and 2020, asking people questions about their experience with motion sickness in cars. Based on the responses of 1165 participants, gender and age showed a strong influence on the self-reported frequency of motion sickness. For deeper analysis, a logistic order regression model was used to estimate the frequency of motion sickness for different user typologies.

Abstract Autonomous vehicles (AVs) are being rapidly introduced into our lives. However, public misunderstanding and mistrust have become prominent issues hindering the acceptance of these driverless technologies. The primary objective of this study is to evaluate the effectiveness of a driving simulator to help the public gain an understanding of AVs and build trust in them. To achieve this aim, we built an integrated simulation platform, designed various driving scenarios, and recruited 28 participants for the experiment. The study results indicate that a driving simulator effectively decreases the participants’ perceived risk of AVs and increases perceived usefulness. The proposed methodologies and findings of this study can be further explored by auto manufacturers and policymakers to provide user-friendly AV design.

Abstract Connected and automated vehicles (CAVs) can bring safety, mobility, and energy benefits to transportation systems. Ideally, CAV applications would be fully evaluated and validated prior to real-world implementation. However, many technical challenges in both software and hardware hinder the process. To comprehensively evaluate all aspects of CAV applications, an integrated evaluation environment is needed with various simulation tools from different domains. In the current literature, there lacks a well-developed interface to enable multi-resolution simulation of vehicle, traffic, virtual environment, and hardware-in-the-loop (HIL) simulation. In this work, a modular and flexible interface is developed to enable multi-resolution vehicle and traffic co-simulation for CAV applications.

Abstract TOC

Abstract Autonomous vehicle technology has become an unprecedented trend in the development of the automobile industry, which can ensure highly efficient use of resources, effectively improve the driving experience, and greatly reduces the driver’s burden. As one of the key technologies of autonomous vehicles, path planning has an important impact on the practical applications of autonomous vehicles. Planning a proper and efficient path is a prerequisite, which can improve the driving experience of autonomous vehicles. Therefore, in-depth research and development on applications of AI technology in path planning definitely have significant value in academic research. In this article, we will introduce a variety of path planning approaches for autonomous vehicles. We summarize the attributes of these path planning algorithms; simultaneously, we analyze the improvements to these algorithms. Then, we have a preliminary discussion on the applications in vehicle positioning and navigation.