Eye Tracking in Driver Attention Research—How Gaze Data Interpretations Influence What We Learn

Eye tracking (ET) has been used extensively in driver attention research. Amongst other findings, ET data have increased our knowledge about what drivers look at in different traffic environments and how they distribute their glances when interacting with non-driving related tasks. Eye tracking is also the go-to method when determining driver distraction via glance target classification. At the same time, eye trackers are limited in the sense that they can only objectively measure the gaze direction. To learn more about why drivers look where they do, what information they acquire foveally and peripherally, how the road environment and traffic situation affect their behavior, and how their own expertise influences their actions, it is necessary to go beyond counting the targets that the driver foveates. In this perspective paper, we suggest a glance analysis approach that classifies glances based on their purpose. The main idea is to consider not only the intention behind each glance, but to also account for what is relevant in the surrounding scene, regardless of whether the driver has looked there or not. In essence, the old approaches, unaware as they are of the larger context or motivation behind eye movements, have taken us as far as they can. We propose this more integrative approach to gain a better understanding of the complexity of drivers' informational needs and how they satisfy them in the moment.

Interdisciplinary Urban Tunnel Control within Smart Cities

Nowadays, urban road tunnels are considered to be independent entities within a city. Their interactions with the rest of the city and vice versa are usually not considered and, if they are, are only considered in a limited way (for example, through the nearest traffic controller). Typically, only the traffic parameters and not the environmental impacts are considered. This paper has two major objectives. First, we provide a systemic view on a road urban tunnel. The major focus is on the interfaces between the tunnel and the rest of the city and the way they will be managed. We are providing a tool to take into consideration a sustainable development of a tunnel (i.e., not only traffic flow parameters such as average speed, but also environmental and societal characteristics). This model expresses the actual traffic situation in a monetary form (i.e., cost of congestions). The second objective is to provide a new road urban tunnel control approach that follows the original methodology and systemic view described in the paper. If the tunnel is controlled autonomously, which corresponds to the current state-of-the-art in many cities, the algorithm decides to close it based on only local parameters. However, the proposed new algorithm takes into consideration not only the traffic situation in the tunnel (expressed by the parameter traffic density), but also the actual traffic situation within the city (expressed by its level of service (LOS)). This allows more environmentally, socially and economically sustainable oriented road urban tunnel management. The described algorithm is demonstrated on a specific example of the tunnel complex Blanka in Prague.

Selecting Remote Driving Locations for Latency Sensitive Reliable Tele-Operation

These days, autonomous vehicles (AVs) technology has been improved dramatically. However, even though the AVs require no human intervention in most situations, AVs may fail in certain situations. In such cases, it is desirable that humans can operate the vehicle manually to recover from a failure situation through remote driving. Furthermore, we believe that remote driving can enhance the current transportation system in various ways. In this paper, we consider a revolutionary transportation platform, where all the vehicles in an area are controlled by some remote controllers or drivers so that transportation can be performed in a more efficient way. For example, road capacity can be effectively utilized and fuel efficiency can be increased by centralized remote control. However, one of the biggest challenges in such remote driving is the communication latency between the remote driver and the vehicle. Thus, selecting appropriate locations of the remote drivers is very important to avoid any type of safety problem that might happen due to large communication latency. Furthermore, the selection should reflect the traffic situation created by multiple vehicles in an area. To tackle these challenges, in this paper, we propose several algorithms that select remote drivers’ locations for a given transportation schedules of multiple vehicles. We consider two objectives in this system and evaluate the performance of the proposed algorithms through simulations. The results show that the proposed algorithms perform better than some baseline algorithms.

MCTS Based Approach for Solving Real-time Railway Rescheduling Problem

Solving a real-time Railway Traffic Management Problem (rtRTMP) is a challenging task for human operators. To solve the traffic situation, many factors need to be considered. Traditionally, the most critical factor is the availability of the possible routes and the relative position of the vehicles to each other. Besides, additional constraints can be found, such as the velocity, the length, and railway company regulations. The human decision-making process is essential in case of any disturbance (deviation from the pre-planned timetable). The human operator may solve this situation, but generally, the solution is not optimal. In this paper, the authors present a new method, where they consider an MCTS based algorithm to solve the traffic situation in a fast way in a given station. The performance of the algorithm is examined in two abstraction levels. The main purpose is to execute an experimental study to examine the efficiency of the MCTS based algorithms to solve railway traffic situations.

Role of Model Predictive Control for Enhancing Eco-Driving of Electric Vehicles in Urban Transport System of Japan

Electrification alters the energy demand and environmental impacts of vehicles, which brings about new challenges for sustainability in the transport sector. To further enhance the energy economy of electric vehicles (EVs) and offer an energy-efficient driving strategy for next-generation intelligent mobility in daily synthetic traffic situations with mixed driving scenarios, the model predictive control (MPC) algorithm is exploited to develop a predictive cruise control (PCC) system for eco-driving based on a detailed driving scenario switching logic (DSSL). The proposed PCC system is designed hierarchically into three typical driving scenarios, including car-following, signal anticipation, and free driving scenario, using one linear MPC and two nonlinear MPC controllers, respectively. The performances of the proposed tri-level MPC-based PCC system for EV eco-driving were investigated by a numerical simulation using the real road and traffic data of Japan under three typical driving scenarios and an integrated traffic situation. The results showed that the proposed PCC system can not only realize driving safety and comfortability, but also harvest considerable energy-saving rates during either car-following (16.70%), signal anticipation (12.50%), and free driving scenario (30.30%), or under the synthetic traffic situation (19.97%) in urban areas of Japan.

Site Selection of Fire Stations in Large Cities Based on Actual Spatiotemporal Demands: A Case Study of Nanjing City

The rapid expansion of cities brings in new challenges for the urban firefighting security, while the increasing fire frequency poses serious threats to the life, property, and safety of individuals living in cities. Firefighting in cities is a challenging task, and the optimal spatial arrangement of fire stations is critical to firefighting security. However, existing researches lack any consideration of the negative effects of the spatial randomness of fire outbreaks and delayed response time due to traffic jams upon the site selection. Based on the set cover location model integrated with the spatiotemporal big data, this paper combines the fire outbreak point with the traffic situation. The presented site selection strategy manages to ensure the arrival of the firefighting task force at random simulated fire outbreak points within the required time, under the constraints of the actual city planning and traffic situation. Taking Nanjing city as an example, this paper collects multi-source big data for the comprehensive analysis, including the full data of the fire outbreak history from June 2014 to June 2018, the traffic jam data based on the Amap, and the investigation data of the firefighting facilities in Nanjing. The regularity behind fire outbreaks is analyzed, the factors related to fire risks are identified, and the risk score is calculated. The previous fire outbreak points are put through the clustering analysis, the spatial distribution probability at points in each cluster is calculated according to the clustering score, and the random fire outbreak points are generated via the Monte Carlo simulation. Meanwhile, the objective emergency response time is set as five minutes. The average vehicle speed for each road in the urban area is calculated, and the actual traffic network model is built to compute the travel time from massive randomly-distributed simulated fire points. The problem is solved by making the travel time for all simulated demand points below five minutes. At last, the site selection result based on our model is adjusted and validated, according to the planned land use. The presented method incorporates the view of the spatiotemporal big data and provides a new idea and technical method for the modification and efficiency improvement of the fire station site selection model, contributing to a service cover ratio increase from 58% to 90%.

Using Car to Infrastructure Communication to Accelerate Learning in Route Choice

The task of choosing a route to move from A to B is not trivial, as road networks in metropolitan areas tend to be over crowded. It is important to adapt on the fly to the traffic situation. One way to help road users (driver or autonomous vehicles for that matter) is by using modern communication technologies.In particular, there are reasons to believe that the use of communication between the infrastructure (network), and the demand (vehicles) will be a reality in the near future. In this paper, we use car-to-infrastructure (C2I) communication to investigate whether the road users can accelerate their learning processes regarding route choice by using reinforcement learning (RL). The kernel of our method is a two way communication, where road users communicate their rewards to the infrastructure, which, in turn, aggregate this information locally and pass it to other users, in order to accelerate their learning tasks. We employ a microscopic simulator in order to compare this method with two others (one based on RL without communication and a classical iterative method for traffic assignment). Experimental results using a grid and a simplification of a real-world network show that our method outperforms both.

Improving the accuracy of calculating the speed of movement at the time of an accident in collisions with incomplete overlap of a part of the car

Introduction. Driving at a speed exceeding the permitted and average speed of traffic flow often leads to a collision of vehicles with other road users or with elements of the arrangement of highways. As a result, it is necessary to establish whether the fact of this violation of the traffic rules, which led to the occurrence of an emergency-dangerous road traffic situation. The methods used to calculate the speed of vehicles based on the resulting deformations are quite accurate, but this fact is true in conditions of complete overlap (impact across the entire width of the front, rear or side parts of the body). But there is a scientific task of developing a methodology according to which an expert or investigator will be able to calculate the average statistical value of measuring the depth of deformation of a vehicle for a specific road traffic situation.Materials and methods. The paper proposes a method for evaluating the possibility of using the data available to the expert for calculation by introducing the coefficient of variation of the depth of penetration. With the help of the coefficient of variation, the specialist has a tool for selecting and ignoring individual measurements of the depth of penetration, depending on the degree of overlap and on the ‘spread’ of the deformation values.Conclusions. After studying a number of collisions with incomplete overlap and excluding the ‘extra’ values of penetration, the speed equivalent to the energy cost for the development of residual deformations and errors (the difference between the true collision speed and the established one without taking into account the ‘falling out’ values of deformations) was calculated and it was found that the use of the algorithm taking into account the coefficient of variation led to sufficiently accurate calculation results.Discussions. The proposed methodology regulates the use of the coefficient of variation as a criterion for the admissibility of the use of source data to determine the quality of the final result of the calculation. This mathematical device is applicable to all collisions, but is especially relevant when studying collisions with incomplete overlap of any part of the car body.