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Abstract Truck platooning comprises a number of trucks equipped with automated lateral and longitudinal vehicle control technology, which allows them to move in tight formation with short following distances. This study is an initial step toward developing an understanding of the occupant injury risks associated with the multiple sequential impacts between truck platoons and roadside safety barriers, regardless of whether the crash is associated with a malfunction of automated control or human operation. Full-scale crash impacts of a tractor-trailer platoon into a concrete bridge guardrail were simulated for a specific Test Level condition according to the Manual for Assessing Safety Hardware (MASH) standards. The model of the bridge barrier was developed based on its drawings, and material properties were assigned according to literature data.

Abstract A number of studies have shown that driving an unfamiliar vehicle has the potential to introduce additional risk, especially for novice drivers. However, such studies have generally used statistical methods based on analyzing crash and near-crash data from a range of driver groups, and therefore the evaluation has the potential to be subjective and limited. For a more objective perspective, this study suggests that it would be worthwhile to consider vehicle dynamic signals obtained from the Controller Area Network (CAN-Bus) and smartphones. This study, therefore, is focused on the effect of driver experience and vehicle familiarity for issues in driver modeling and distraction. Here, a group of 20 drivers participated in our experiment, with 13 of them having participated again after a one-year time lapse in order for analysis of their change in driving performance.

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 In today’s competitive automobile market, driver comfort is at utmost importance and the bar is being raised continuously. Gear Shifting is a crucial customer touch point. Any issue or inconvenience caused while shifting gear can result into customer dissatisfaction and will impact the brand image. While there are continual efforts being taken by most of the car manufactures, “Double Bump” in gearshift has remained as a pain area and impact severely on the shift feel. This is more prominent in North-South (N-S) transmissions. In this paper ‘Double Bump’ is a focus area and a mathematical / analytical approach is demonstrated by analyzing ‘impacting parameters’ and establishing their co-relation with double bump. Additionally, the results are also verified with a simulation model.

Abstract Commercial vehicles contribute to the majority of freight transportation in the United States. They are also significant fuel consumers, with over 23% of fuel used in transportation in the United States. The gas price volatility and increasingly stringent regulation on greenhouse-gas emissions have driven manufacturers to adopt new fuel-efficient technologies. Among others, an advanced transmission control strategy, which can provide tangible improvement with low incremental cost. In the commercial sector, individual drivers have little or no interest in vehicle fuel economy, contrary to fleet owners. Aggressive driving behavior can greatly increase the real-world vehicle fuel consumption. However, the effectiveness of transmission calibration to match the shift strategy to the driving characteristics is still a challenge.

Abstract When commercial vehicles drive in a mountainous area, the complex road condition and long slopes cause frequent acceleration and braking, which will use 25% more fuel. And the brake temperature rises rapidly due to continuous braking on the long-distance downslopes, which will make the brake drum fail with the brake temperature exceeding 308°C [1]. Meanwhile, the kinetic energy is wasted during the driving progress on the slopes when the vehicle rolls up and down. Our laboratory built a model that could calculate the distance from the top of the slope, where the driver could release the accelerator pedal. Thus, on the slope, the vehicle uses less fuel when it rolls up and less brakes when down. What we do in this article is use this model in a real vehicle and measure how well it works.

Abstract A key element in an ergonomically designed driver’s seat in a car is the correct identification of driver seating position and posture accommodation. Current practice by the automotive Original Equipment Manufacturer (OEM) is to utilize the Society of Automotive Engineering (SAE) J1517 standard practice as a reference. However, it was found that utilizing such guidelines, which were developed based on the American population, did not fit well with the anthropometry and stature of the Malaysian population. This research seeks to address this issue by comparing the SAE J1517 Model against Malaysian preferred driving position. A total of 62 respondents were involved for the driver seating position and accommodation study in the vehicle driver’s seat buck mockup survey and measurements. The results have shown that the Malaysian drivers prefer to sit forward as compared to the SAE J1517 Model and have shorter posture joint angle.

Abstract The advancement towards development of autonomy follows either the bottom-up approach of gradually improving and expanding existing Advanced Driver Assist Systems (ADAS) technology where the driver is present in the control loop or the top-down approach of directly developing autonomous vehicle hardware and software using alternative approaches without the driver present in the control loop. Most ADAS systems today fall under the classification of SAE Level 1 which is also referred to as the driver assistance level. The progression from SAE Level 1 to SAE Level 2 or partial automation involves the critical task of merging automated lateral control and automated longitudinal control such that the tasks of steering and acceleration/deceleration are not required to be handled by the driver under certain conditions [1].

Abstract To evaluate that automated vehicle is as safe as a human driver, a following question is studied: how does an automated vehicle react under extreme conditions close to collision? In order to understand the collision avoidance capability of an automated vehicle, we should analyze not only such post-extreme condition behavior but also pre-extreme condition behavior. We present a theory to analyze the collision avoidance capability of automated driving technologies. We also formulate a collision avoidance equation on the theory. The equation has two types of solutions: response driving plans and preparation driving plans. The response driving plans are supported by response strategy on which the vehicle reacts after detection of a hazard and they are highly efficient in terms of travel time.

Abstract Modern driver compartment restraint systems have at least three key components that work together: safety belt system, airbags, and collapsible steering column. During a crash, a steering column will collapse at a predetermined force called breakaway force. Once the force of a crash has reached the breakaway force threshold, the column will move towards the motor area. When the column moves, the drivers’ peak forces and acceleration are decreased because the time and distance that are given to decelerate are increased. The usage of a breakaway force element inside the steering column allows car manufacturers to control the movement of the steering column at a certain point during a crash. Any load below the breakaway force, such as airbag deployment and normal or misuse forces applied by the driver, is absorbed by the system. Today’s force-based systems are optimized (design/configure) using various crash configurations, leading to one specific behavior of the column.

Abstract To solve the problem that it is difficult for drivers to control the vehicle at low speed, a new setting scheme of pedal map is proposed to ensure that the vehicle has the speed controllability in the full speed range. In this scheme, based on obtaining the maximum and minimum driving characteristics of the vehicle and the driving resistance characteristics of the vehicle, the pedal map is divided into a sensitive area and insensitive area. In the insensitive area, acceleration hysteresis is formed, which ensures that the throttle is slightly fluctuated and has good speed stability. At the same time, the sensitive area of the accelerator pedal is formed far away from the driving resistance curve to ensure that the vehicle has a great acceleration ability. To verify the effectiveness of the proposed scheme, the data of a commercial vehicle is selected for the design of the pedal map, and the driver-vehicle closed-loop test based on the driving simulator is conducted.

Abstract Driving skills and, more in general, driver’s behavior may have a major impact on vehicle performances. They can affect not only the fuel consumption of the machine but, at the same time, also its productivity and the durability of many mechanical, electronic, and hydraulic components equipped on the vehicle. In this article, a model, able to reproduce different driver’s approaches to the machine, is shown. The longitudinal driver model has been developed in Matlab/Simulink and, firstly, employed on buses and trucks applications; then it has been also exported into a wheel loader plant model designed in Simcenter AMESim. The article is focused on how the driver model, integrated into the wheel loader plant model, can simulate custom cycles with a different driving style (high/low aggressiveness). It allows, on one hand, to emulate a real driver behavior and, on the other hand, to increase simulation repeatability and reproducibility.

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 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 Letter from the Guest Editors

Abstract The driving safety performance of autonomous driving vehicles must be ensured before on-road implementation. Because it is not realistic to evaluate every single test condition in real-world traffic, computer simulation methods can be used. The driving safety performance can be evaluated by simulating various driving scenarios and calculating surrogate indicators representing dangerous collision risk. This study used a near-miss database and introduced a surrogate indicator that represents a potential risk in the driving scenarios for rear-end and cut-in collisions. The near-miss video database includes several driving scenarios experienced by human drivers, such as dangerous situations that lead to accidents, potentially dangerous situations that have a risk probability to escalate into dangerous situations, and normal driving situations. A skilled and attentive human driver foresees dangerous situations while driving and avoids them.

Abstract Contemporary research has found differences between demographic groups in their stated instrument cluster component design preferences. For instance, elderly drivers prefer large icons and textual displays of information, while younger drivers preferred gauges to display information. The purpose of this study was to evaluate whether instrument clusters, designed for specific demographic groups, would facilitate safe driving behavior and solicit higher evaluation scores in their targeted demographics. Fifty participants, consisting of 30 elderly and 20 younger drivers (gender-balanced), completed a series of tasks to retrieve information from the instrument cluster while driving a high-fidelity simulator. Participants’ driving behavior, response time, subjective ratings, and a semi-structured post-experimental interview on different cluster designs were collected to evaluate each instrument cluster design.

Abstract This article presents data mining methodologies designed to support data-driven, long-term, and large-scale research in the areas of in-vehicle monitoring, learning, and assessment of older adults’ driving behavior and physiological signatures under a set of well-defined driving scenarios. The major components presented in the article include the instrumentation of an easily transportable vehicle data acquisition system (VDAS) designed to collect multimodal sensor data during naturalistic driving, an ontology that enables the study of driver behaviors at different levels of integration of semantic heterogeneity into the driving context, and a driving trip segmentation algorithm for automatically partitioning a recorded real-world driving trip into segments representing different types of roadways and traffic conditions.

Abstract Preview control algorithm has been widely implemented in intelligent vehicle path-tracking controllers. The key challenge of developing such control is to determine the appropriate preview distance, which plays a vital role in achieving the optimal trade-off between two competing control objectives, tracking accuracy and driving stability. Additionally, vehicle speed and road radius have a significant impact on the optimal preview distance. Thus a hierarchical vehicle path-tracking control strategy based on the adaptive two-point preview is proposed in this article. In the upper-layer module, the two-point preview driver model is constructed to obtain the target yaw rate according to the comprehensive deviation. In the lower-layer module, the neural network sliding mode controller is employed to track the yaw rate and, therefore, achieve intelligent vehicle self-tracking.

Abstract Objectified analysis and evaluation tools offer cost- as well as time-saving potentials regarding the calibration process of vehicle control units. To reduce the time required for the calibration effort, standardized processes including the frontloading of development tasks enable swift calibration procedures and can be used to develop a basis for the comparison of different vehicles and also the calibration quality. In this environment, objectified evaluation methods are also being developed for the investigation of the drivability of electric vehicles. This article presents a methodology for assessing the longitudinal drive behavior of battery electric vehicles during deceleration maneuvers. The aim is to objectively evaluate the vehicle deceleration by means of reproducible driving maneuvers. In addition to further measurement signals, the longitudinal acceleration signal serves as the main evaluation basis.