Operation Of Automated Vehicles Research Articles

Freeway merging trajectory prediction for automated vehicles using naturalistic driving data

Due to differences in speed and the complex interaction between merging and through-lane vehicles at freeway merging sections, crashes involving both human drivers and automated vehicles (AVs) persist. To assist AVs to predict the intentions of surrounding vehicles for further dynamic motion planning, researchers have focused on developing trajectory prediction algorithms. Few studies, however, have developed merging trajectory prediction models using naturalistic driving data in China, making it urgent to put it on the agenda for AVs’ safety and efficiency at freeway merging sections. Based on merging periods extracted from the Shanghai Naturalistic Driving Study (SH-NDS), this study compares merging behavior on freeways with through-lane speed limits of 80 km/h, 100 km/h, and 120 km/h using analysis of variance (ANOVA). Merging trajectory prediction algorithms for these three speed limit cases are trained and tested using backpropagation neural network (BPNN) and long short-term memory neural network (LSTM NN) approaches. Results show 1) significant differences among the three cases in all merging behavior variables except for longitudinal gap; and that 2) the BPNN algorithm for merging trajectory prediction demonstrates superior performance compared to the LSTM NN. Two major contributions to the safe operation of AVs are provided: 1) the developed algorithms can be integrated into AV systems to accurately predict real-time desired trajectories of nearby merging vehicles in uncongested traffic conditions, and assist ongoing motion planning strategies for AVs; and 2) the algorithms can be incorporated in simulation tests for AV safety evaluation involving freeway merging sections.

Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations

Agent-based simulations of shared automated vehicle operations: reflecting travel-party size, season and day-of-week demand variations

Effective remote automated vehicle operation: a mixed reality contextual comparison study

Effective remote automated vehicle operation: a mixed reality contextual comparison study

A multinational empirical study of perceived cyber barriers to automated vehicles deployment

The digital transformation of Automated Vehicles (AVs) has raised concerns in the cyber realm among prospective AV consumers. However, there is a dearth of empirical research on how cyber obstacles may impact the operation of AVs. To address this knowledge gap, this study examines the six critical cyber impediments (data privacy, AV connectivity, ITS infrastructure, lack of cybersecurity regulations, AV cybersecurity understanding, and AV cyber-insurance) that influence the deployment of AVs. The impact of gender, age, income level, and individual AV and cybersecurity knowledge on these obstacles are statistically assessed using a sample of 2061 adults from the United States, the United Kingdom, New Zealand, and Australia. The research revealed intriguing empirical findings on all cyber barriers in the form of a trichotomy: participants' education level, understanding of AVs, and cybersecurity knowledge. As education levels increase, the significance of a cyber barrier to AV deployment decreases; however, as AV comprehension and cybersecurity knowledge increase, the perception of a cyber barrier becomes significantly more important. In addition, the study demonstrates differences in perceptions of cyber barriers and AV deployments based on gender, age, income, and geographic location. This study's findings on cyber barriers and AV deployment have implications for academia and industry.

Applying a semi-automated workflow for digital dynamic maps to support the operation of automated vehicles

Automated vehicles (AVs) promise new opportunities for urban transport and potential benefits to road safety, transport efficiency, travel comfort and emission levels. However, substantial technical hurdles need to be overcome before AVs can operate regularly on urban streets and highways. Besides considerable infrastructure adaptations, one way to enable the operation of AVs are digital dynamic maps (DDM or DD-map), which combine a high-definition map of the physical road scape with dynamic road data obtained from digital sources. Here we present the results of the EU Project SHOW on digital dynamic maps and their relevance for city planning, in particular how cities can benefit from the interplay with map providers. We provide a workflow to set-up and operate a digital dynamic map and discuss how this might benefit transportation systems.

Operational design domain of automated vehicles at freeway entrance terminals

The safe operation of automated vehicles (AVs) is now on the research agenda, with attention to the AV’s operational design domain (ODD), which defines the conditions in which its driving automation system is designed to function. Due to the limited sight line on freeway entrance terminals, crashes involving AVs continue to occur during the merging operation. Considering that detecting range and detecting angle are critical parameters for the AV sensor’s sight triangle and may differ from human driver requirements, it is urgent to provide initial ODD results to improve the AV’s detection capabilities at entrance terminals. Based on sight triangle requirements, this study took a mathematical approach to quantify the relationships among AV sensor capabilities, design speed, and geometric design indicators separately for flat grades and 5 % grades. Required stopping sight distance (SSD) for the merging AV and travelled distance of the through-lane vehicle were calculated based on vehicle kinematics, common sensor capabilities, and Green Book parameter values. Using the law of cosines, the required detecting range and the detecting angle for AV sensors were calculated at various ramp and through-lane design speed combinations as the ODD constraint indicators. Two types of entrance terminals, the taper and the parallel, were considered in this study. Results show: 1) the maximum required detecting range and angle for AV sensors reached 185.0 m and 179.9 degrees, respectively; 2) the required detecting range and angle have only minor differences between the taper and parallel types; 3) the minimum acceleration lane length suggested for human drivers may not be available for AVs. These results provide two major contributions to safe operation of AVs: 1) for roadway design, the results provide general design references for acceleration lane length, merging angle, and sight-distance guarantee at freeway entrance terminals; and 2) for ODD management, results provide theoretical support for governments and traffic management departments to regulate AVs at these terminals, in some cases limiting the AV to manual driving or lower speed limits.

Situation-Aware Left-Turning Connected and Automated Vehicle Operation at Signalized Intersections

One challenging aspect of the connected and automated vehicle (CAV) operation in mixed traffic is the development of a situation-awareness module for CAVs. While operating on public roads, CAVs need to assess their surroundings, especially the intentions of non-CAVs. Generally, CAVs demonstrate a defensive driving behavior, and CAVs expect other nonautonomous entities on the road will follow the traffic rules or common driving behavior. However, the presence of aggressive human drivers in the surrounding environment, who may not follow traffic rules and behave abruptly, can lead to serious safety consequences. In this article, we have addressed the CAV and non-CAV interaction by evaluating a situation-awareness module for left-turning CAV operations in an urban area. Existing literature does not consider the intent of the following vehicle for a CAV's left-turning movement, and existing CAV controllers do not assess the following non-CAV's intents. Based on our simulation study, the situation-aware CAV controller module reduces up to 47% of the abrupt braking of the following non-CAVs for scenarios with different opposing through movement compared to the base scenario with the autonomous vehicle, without considering the following vehicle's intent. The analysis shows that the average travel time reductions for the opposite through traffic volumes of 600, 800, and 1000 vehicle/hour/lane are about 58%, 52%, and 62%, respectively, considering both scenarios for the aggressive human driver following the CAV if the following vehicle's intent is considered by a CAV in making a left-turn at an intersection.

Demonstrations of Cooperative Perception: Safety and Robustness in Connected and Automated Vehicle Operations.

Cooperative perception, or collective perception (CP), is an emerging and promising technology for intelligent transportation systems (ITS). It enables an ITS station (ITS-S) to share its local perception information with others by means of vehicle-to-X (V2X) communication, thereby achieving improved efficiency and safety in road transportation. In this paper, we present our recent progress on the development of a connected and automated vehicle (CAV) and intelligent roadside unit (IRSU). The main contribution of the work lies in investigating and demonstrating the use of CP service within intelligent infrastructure to improve awareness of vulnerable road users (VRU) and thus safety for CAVs in various traffic scenarios. We demonstrate in experiments that a connected vehicle (CV) can “see” a pedestrian around the corners. More importantly, we demonstrate how CAVs can autonomously and safely interact with walking and running pedestrians, relying only on the CP information from the IRSU through vehicle-to-infrastructure (V2I) communication. This is one of the first demonstrations of urban vehicle automation using only CP information. We also address in the paper the handling of collective perception messages (CPMs) received from the IRSU, and passing them through a pipeline of CP information coordinate transformation with uncertainty, multiple road user tracking, and eventually path planning/decision-making within the CAV. The experimental results were obtained with manually driven CV, fully autonomous CAV, and an IRSU retrofitted with vision and laser sensors and a road user tracking system.

Development of an Algorithm for Optimal Demand\xa0Responsive Relocatable Feeder Transit Networks Serving Multiple Trains and Stations

Although demand responsive feeder bus operation is possible with human-driven vehicles, it has not been very popular and is mostly available as a special service because of its high operating costs due to intensive labor costs as well as requirement for advanced real-time information technology and complicated operation. However, once automated vehicles become available, small-sized flexible door-to-door feeder bus operation will become more realistic, so preparing for such automated flexible feeder services is necessary to take advantage of the rapid improvement of automated vehicle technology. Therefore, in this research, an algorithm for optimal flexible feeder bus routing, which considers relocation of buses for multiple stations and trains, was developed using a simulated annealing algorithm for future automated vehicle operation. An example was developed and tested to demonstrate the developed algorithm. The algorithm successfully handled relocation of buses when the optimal bus routings were not feasible using the buses available at certain stations. Furthermore, the developed algorithm limited the maximum degree of circuity for each passenger while minimizing the total cost, including total vehicle operating costs and total passenger in-vehicle travel time costs.

Driving aggressiveness management policy to enhance the performance of mixed traffic conditions in automated driving environments

The advancement of vehicular technologies for automated driving will lead to a mixed traffic flow that depends on the interaction between automated vehicles (AVs) and manually driven vehicles (MVs) because the market penetration rate (MPR) of AVs will gradually increase over time. Because automated driving environments provide us with a valuable opportunity for controlling individual vehicle operation, a strategic policy for managing AVs operation is expected to enhance the performance of the traffic stream. Therefore, the operation of AVs needs to be properly determined to cope with various traffic and road conditions and thereby facilitate smooth and effective vehicle interactions. This study proposed a novel traffic management strategy in automated driving environments by adjusting the driving aggressiveness of AV operation, defined as automated driving aggressiveness (AuDA). VISSIM microscopic simulation experiments were conducted to derive the proper AuDAs to enhance both the traffic safety and the mobility performance. Traffic conflict rates and average travel speeds were used as indicators for the performance of safety and operations. While conducting the simulations, the level of service (LOS) and MPR of the AVs were also considered. In addition, the relationship between key variables for adaptive cruise control (ACC) operations and AuDA policies was explored to better support the understanding of how the proposed methodology works in practice. Promising results showed that the proposed methodology would be effective in optimizing the performance of mixed traffic conditions. Furthermore, the outcome will be valuable in developing various policies and guidelines to manage the operation of AV in automated driving environments.

Open pit mine truck robotization at the stage of rock transportation

The prerequisites for robotization of such a component stage of mining as mined rock transportation are considered in the article. Digitization of dump truck positioning, maneuvering and 3D map tracking forms the basis of approaching robotization. The equipment required for accident prevention in an open pit is discussed. A simplified algorithm of automated vehicle operation is offered.

An optimization model for vehicle routing of automated taxi trips with dynamic travel times

Abstract In this paper, we propose a method of automated vehicle operation in taxi systems that addresses the problem of associating trips to automated taxis (ATs) and assigning those vehicles to paths on an urban road network. This system is envisioned to provide a transport service within a city area with a seamless door-to-door connection for all passengers’ origins and destinations. ATs can drive themselves on the roads with reduced direct human input, which allow taxis to satisfy the next trip or park themselves while waiting for a request if needed. We propose an integer programming model to define the routing of the vehicles according to a profit maximization function while depending on dynamic travel times which vary with the flow of the ATs. This will be especially important when the number of automated vehicles circulating on the roads is so high that will cause traffic congestion. The total profit involves the system revenue, vehicle fuel costs, vehicle depreciation costs, parking costs, penalties for unsatisfied trips and passengers’ congestion delay. The model is applied to a small case study and the results allow assessing the impact of the ATs movements on traffic congestion and the profitability of the system. Even with a small case study, it is possible to conclude that having in consideration the effect of the vehicle flows on travel time leads to different results in terms of the system profit, the parking cost and the driving distance which points out the importance of this type of models.

Automated Vehicle Operation Characteristics with Overhead View Monitoring.

It is advantageous to apply overhead view monitoring of a vehicle and its surroundings to automatically navigate a vehicle, and for human intervention if necessary. Here, human operation is investigated to incorporate it's characteristics into automatic operation. Computer simulation programs which deal with the acquisition of human ideas, in overhead view monitoring, for regulating the movement of a vehicle and for collision avoidance, are adopted. Operations by human operators (subjects) are experienced by the automatic navigation system as sets of instances in the form of (vehicle state, steering angle). The effectiveness of human operation is discussed and the characteristics in the results are analyzed.