Non-automated Vehicles Research Articles

Understanding key factors in automated vehicle collisions: Automating data extraction and analyzing key insights using explainable AI

With the growing development and deployment of automated vehicles (AVs), it is crucial to understand the associated risks and factors contributing to collisions involving AVs. California possesses an immense amount of publicly available data from AV testing due to the requirement laid out by the California Department of Motor Vehicles (DMV), which requires all automated vehicle operators to report collisions for any level of severity. However, this information is reported in specific forms and requires a laborious task to aggregate data from these reports. This study creates an automated data extraction system for these reports and analyses collision characteristics using logistic regression models as well as XGBoost models with SHapley Additive exPlanation (SHAP) interpretation. Additionally, these characteristics are matched with the characteristics of non-automated vehicles (non-AVs) for the same region. The results of the study indicate that rear-end collisions are the most common type of collision observed in currently deployed AVs. The analysis further revealed an increased likelihood of injury-prone rear-end collisions in AVs at intersections compared to non-AVs. Transportation policymakers and researchers should take these safety concerns into account when addressing AV deployment and developing appropriate measures to mitigate collision risks in mixed fleet conditions.

Synthesizing fatal crashes involving partially automated vehicles and comparing with fatal crashes involving non-automated vehicles

Automated vehicles (AVs) are expected to improve safety by gradually reducing human decisions while driving. However, there are still questions on their effectiveness as we transition from almost 0% AVs to 100% AVs with different levels of vehicle autonomy. The focus of this study, therefore, is on synthesizing and identifying risk factors influencing fatal crashes involving partially automated vehicles (PAVs) i.e., level 1 AVs and level 2 AVs, in the United States. Fatal crashes involving non-AVs (level 0 vehicles) within their vicinity were used for comparison, to minimize unobserved heterogeneity and randomness associated with the influencing risk factors. The fatal crash data for the years 2016 to 2019 is used for the analysis. A partial proportional odds model is developed using crash, road, and vehicle characteristics as independent variables while the fatal crash involving a vehicle with a specific level of automation (0, 1, or 2) is used as the dependent variable. The level of automation was captured by developing tools and identifying advanced features in each vehicle based on the vehicle identification number (VIN). The odds ratios varied for PAVs compared to non-AVs. PAVs are safer but are more likely to be involved in a fatal crash with non-motorists. Pedestrian automatic emergency braking (PAEB) and lane-keeping assistance (LKA) were observed to improve safety by reducing possible collision with a pedestrian and roadside departure, respectively. Contrarily, vehicles with other smart features are still highly likely to be involved in fatal crashes, demanding further research and attention.

Behavioral adaptation of drivers when driving among automated vehicles

Purpose This paper aims to explore whether drivers would adapt their behavior when they drive among automated vehicles (AVs) compared to driving among manually driven vehicles (MVs).Understanding behavioral adaptation of drivers when they encounter AVs is crucial for assessing impacts of AVs in mixed-traffic situations. Here, mixed-traffic situations refer to situations where AVs share the roads with existing nonautomated vehicles such as conventional MVs. Design/methodology/approach A driving simulator study is designed to explore whether such behavioral adaptations exist. Two different driving scenarios were explored on a three-lane highway: driving on the main highway and merging from an on-ramp. For this study, 18 research participants were recruited. Findings Behavioral adaptation can be observed in terms of car-following speed, car-following time gap, number of lane change and overall driving speed. The adaptations are dependent on the driving scenario and whether the surrounding traffic was AVs or MVs. Although significant differences in behavior were found in more than 90% of the research participants, they adapted their behavior differently, and thus, magnitude of the behavioral adaptation remains unclear. Originality/value The observed behavioral adaptations in this paper were dependent on the driving scenario rather than the time gap between surrounding vehicles. This finding differs from previous studies, which have shown that drivers tend to adapt their behaviors with respect to the surrounding vehicles. Furthermore, the surrounding vehicles in this study are more “free flow'” compared to previous studies with a fixed formation such as platoons. Nevertheless, long-term observations are required to further support this claim.

Feudalistic Platooning: Subdivide Platoons, Unite Networks, and Conquer Efficiency and Reliability.

Cooperative intelligent transportation systems (C-ITSs) such as platooning rely on a robust and timely network that may not always be available in sufficient quality. Out of the box hybrid networks only partly eliminate shortcomings: mutual interference avoidance, data load balancing, and data dissemination must be sophisticated. Lacking network quality may lead to safety bottlenecks that require that the distance between the following vehicles be increased. However, increasing gaps result in efficiency loss and additionally compromise safety as the platoon is split into smaller parts by traffic: maneuvers, e.g., cut-in maneuvers bear safety risks, and consequently lower efficiency even further. However, platoons, especially if they are very long, can negatively affect the flow of traffic. This mainly applies on entry or exit lanes, on narrow lanes, or in intersection areas: automated and non-automated vehicles in traffic do affect each other and are interdependent. To account for varying network quality and enable the coexistence of non-automated and platooned traffic, we present in this paper a new concept of platooning that unites ad hoc—in form of IEEE 802.11p—and cellular communication: feudalistic platooning. Platooned vehicles are divided into smaller groups, inseparable by surrounding traffic, and are assigned roles that determine the communication flow between vehicles, other groups and platoons, and infrastructure. Critical vehicle data are redundantly sent while the ad hoc network is only used for this purpose. The remaining data are sent—relying on cellular infrastructure once it is available—directly between vehicles with or without the use of network involvement for scheduling. The presented approach was tested in simulations using Omnet++ and Simulation of Urban Mobility (SUMO).

Driver Response Time in Cut-Off Scenarios from the Second Strategic Highway Research Program Naturalistic Database

A cut-in or cut-off scenario involves a vehicle intruding into the path of another vehicle traveling in the same direction. These lane changes can lead to potentially dangerous situations, either a sideswipe or a rear-end crash. In this study, 552 cut-in events were analyzed, including four crash and 548 near-crash events from the Second Strategic Highway Research Program (SHRP-2) data set. Video and onboard-data-recorder data from the responding vehicle were used to analyze various factors associated with drivers’ responses. Driver response times were measured from three different event onsets, and the effects of different factors on the respective response times were measured. These factors included the behavior of the subject driver, the behavior of the intruding vehicle/principal other vehicle (POV), and different environmental and infrastructural factors. The results showed that drivers responded more slowly when the POV took longer to move laterally to the subject driver’s lane edge and faster when this time was short. Similarly, drivers responded faster to merging vehicles that started from a stop. Yet, response times were no different when the POV utilized a directional signal. These results point to a kinematic threshold involving lateral distance and lateral speed that best describes how drivers were triggered to respond. Drivers also responded faster near intersections, and at night. The results can be utilized to design crash mitigation systems in autonomous vehicles, as well as non-automated vehicles, to supplement human responses where their abilities may be lacking.

Addressing inequal risk exposure in the development of automated vehicles

Automated vehicles (AVs) are expected to operate on public roads, together with non-automated vehicles and other road users such as pedestrians or bicycles. Recent ethical reports and guidelines raise worries that AVs will introduce injustice or reinforce existing social inequalities in road traffic. One major injustice concern in today’s traffic is that different types of road users are exposed differently to risks of corporal harm. In the first part of the paper, we discuss the responsibility of AV developers to address existing injustice concerns regarding risk exposure as well as approaches on how to fulfill the responsibility for a fairer distribution of risk. In contrast to popular approaches on the ethics of risk distribution in unavoidable accident cases, we focus on low and moderate risk situations, referred to as routine driving. For routine driving, the obligation to distribute risks fairly must be discussed in the context of risk-taking and risk-acceptance, balancing safety objectives of occupants and other road users with driving utility. In the second part of the paper, we present a typical architecture for decentralized automated driving which contains a dedicated module for real-time risk estimation and management. We examine how risk estimation modules can be adjusted and parameterized to redress some inequalities.

Anticipatory Driving in Automated Vehicles: The Effects of Driving Experience and Distraction.

To understand the influence of driving experience and distraction on drivers' anticipation of upcoming traffic events in automated vehicles. In nonautomated vehicles, experienced drivers spend more time looking at cues that indicate upcoming traffic events compared with novices, and distracted drivers spend less time looking at these cues compared with nondistracted drivers. Further, pre-event actions (i.e., proactive control actions prior to traffic events) are more prevalent among experienced drivers and nondistracted drivers. However, there is a research gap on the combined effects of experience and distraction on driver anticipation in automated vehicles. A simulator experiment was conducted with 16 experienced and 16 novice drivers in a vehicle equipped with adaptive cruise control and lane-keeping assist systems (resulting in SAE Level 2 driving automation). Half of the participants in each experience group were provided with a self-paced primarily visual-manual secondary task. Drivers with the task spent less time looking at cues and were less likely to perform anticipatory driving behaviors (i.e., pre-event actions or preparation for pre-event actions such as hovering fingers over the automation disengage button). Experienced drivers exhibited more anticipatory driving behaviors, but their attention toward the cues was similar to novices for both task conditions. In line with nonautomated vehicle research, in automated vehicles, secondary task engagement impedes anticipation while driving experience facilitates anticipation. Though Level 2 automation can relieve drivers of manually controlling the vehicle and allow engagement in distractions, visual-manual distraction engagement can impede anticipatory driving and should be restricted.

Addressing EU climate targets: Reducing CO2 emissions using cooperative and automated vehicles

The European Union (EU) has set rather ambitious goals for reducing emissions and improving related environmental concerns. The majority of EU effort towards reducing emissions is aimed at vehicle manufacturers, mandating development of more efficient engines in order to reduce emissions. We propose a complementary approach to help reach the EU goals. Due to the need for motorists to repeatedly stop and accelerate while driving in urban areas, overall emission rates are observed to be higher in these environments than on open motorways. As a result, management algorithms that focus on harmonising the flow of traffic in urban centres have the potential to greatly reduce urban emission rates. In this paper, we propose a set of functionalities in form of ”use cases” in order to illustrate the inefficiencies of urban traffic flow by utilising cooperation between vehicles and infrastructure. The major contribution of this paper is in addressing the potential positive impact of such measures. The results and the expected effects of the algorithms were determined using a microscopic traffic simulation model. Importantly, this approach also addresses the transition phase, when only a limited number of cooperative and automated vehicles11Please note that the terminology in the field varies. Within this paper, we adopted definition according to https://www.andata.at/en/answer/whats-the-difference-between-autonomous-automated-connected-and-cooperative-driving.html. (CAVs) will be introduced into traffic, resulting in a mix of automated and non-automated vehicles on the roadways. We demonstrate that introducing CAVs into traffic can lead to significant progress towards EU emissions targets, even for lower penetration levels. According to our models, full use of CAVs on roadways would lead to a CO2 emissions decrease of 10–19%.

Differences in situational awareness between elderly and middle-aged drivers in level 2 automated vehicles versus nonautomated vehicles

Abstract This study explores how driving with Level 2 automation affects situational awareness. The experiment participants drove on an expressway in a Level 2 automated vehicle and then in a nonautomated vehicle for 30 ​min per vehicle. Fifteen middle-aged drivers and 15 elderly drivers participated. The experimental vehicle was a 2016 Nissan Serena equipped with the Society of Automated Engineers’ Level 2 automated driving system. In all runs, the most common cause of driver override was vehicles merging from on-ramps. Video recordings of the drivers’ gaze showed that elderly drivers’ situational awareness might be lower during automated driving than conventional driving. In a subjective survey, the middle-aged drivers reported difficulty in maintaining situational awareness during automated driving. To prevent accidents at on-ramps due to drivers’ reduced situational awareness, roads and the automated vehicle systems must be improved to mitigate losses in situational awareness.

Traffic dynamics under speed disturbance in mixed traffic with automated and non-automated vehicles

This paper elucidates the impacts of vehicle heterogeneity on traffic dynamics and throughput of mixed traffic consisting of connected automated vehicles (CAVs) and regular vehicles (RVs). The main premise is that the heterogeneity in preferred acceleration rate, desired speed, and car-following (CF) behavior (e.g., reaction pattern and sensitivity to a traffic disturbance) will change traffic properties in ways that can undermine traffic flow throughput. This paper first decomposes the mechanism into two elements – one driven by acceleration and one by time-varying CF response to disturbances – and then investigates their compounded effect. This paper also provides unifying frameworks to analyze the behavior of RVs and CAVs to facilitate analytical investigations. The results reveal how heterogeneous acceleration and CF behavior may create persistent voids and diminish traffic throughput. Integrating all the elements, throughput reduction is quantified via numerical simulations.

Traffic dynamics under speed disturbance in mixed traffic with automated and non-automated vehicles

This paper elucidates the impacts of vehicle heterogeneity on traffic dynamics and throughput of mixed traffic consisting of connected automated vehicles (CAVs) and regular vehicles (RVs). The main premise is that the heterogeneity in preferred acceleration rate, desired speed, and car-following (CF) behavior (e.g., reaction pattern and sensitivity to a traffic disturbance) will change traffic properties in ways that can undermine traffic flow throughput. This paper first decomposes the mechanism into two elements – one driven by acceleration and one by time-varying CF response to disturbances – and then investigates their compounded effect. This paper also provides unifying frameworks to analyze the behavior of RVs and CAVs to facilitate analytical investigations. The results reveal how heterogeneous acceleration and CF behavior may create persistent voids and diminish traffic throughput. Integrating all the elements, throughput reduction is quantified via numerical simulations.

Uncovering early adopter’s perceptions and purchase intentions of automated vehicles: Insights from early adopters of electric vehicles in California

Research on vehicle automation is one of the most current topics in transportation. Some of the questions plaguing the research community include design, cost, and adoption. Many of these questions will remain unanswered until automated vehicles are available to the consumer. In this study, we use a sample of California new electric vehicle buyers to understand if and how current adopters of new vehicle technologies will adopt automated vehicles. We find that many respondents are interested in purchasing an automated vehicle but indicate that they only have average knowledge of the technology. Using an ordinal logit model, we model the interest in purchasing a fully-automated vehicle and find that younger men who purchase higher cost vehicles are more interested in purchasing a fully-automated vehicle. Above all else, those who perceive automated vehicles as being safer than non-automated vehicles have an interest in purchasing an automated vehicle.

A Platooning Strategy for Automated Vehicles in the Presence of Speed Limit Fluctuations

Platooning is expected to enhance the efficiency of operating automated vehicles. The positive impacts of platooning on travel time reliability, congestion, emissions, and energy consumption have been shown for homogenous roadway segments. However, the transportation system consists of inhomogeneous segments, and understanding the full impacts of platooning requires investigation in a realistic setup. One of the main reasons for inhomogeneity is speed limit fluctuations. Speed limit changes frequently throughout the transportation network, due to safety-related considerations (e.g., changes in geometry and workzone operations) or congestion management schemes (e.g., speed harmonization systems). In the current transportation systems with human-driven vehicles, these speed drops can potentially result in shockwave formation, which can cause travel time unreliability. Automated vehicles, however, have the potential to prevent shockwave formation and propagation and, therefore, enhance travel time reliability. Accordingly, this study presents a constant time headway strategy for automated vehicle platooning to ensure accurate tracking of any velocity profile in the presence of speed limit fluctuations. The performance of the presented platooning strategy is compared with Gipps’ car-following model and intelligent driver model, as representatives of regular non-automated vehicles. Simulation results show that implementing a fully autonomous system prevents shockwave formation and propagation, and enhances travel time reliability by accurately tracking the desired velocity profile. Moreover, the performance of platoons of regular and automated vehicles is investigated in the presence of a speed drop. The results show that as the market penetration rate of automated vehicles increases, the platoon can track the velocity profile more accurately.

Isolated intersection control for various levels of vehicle technology: Conventional, connected, and automated vehicles

Connected vehicle technology can be beneficial for traffic operations at intersections. The information provided by cars equipped with this technology can be used to design a more efficient signal control strategy. Moreover, it can be possible to control the trajectory of automated vehicles with a centralized controller. This paper builds on a previous signal control algorithm developed for connected vehicles in a simple, single intersection. It improves the previous work by (1) integrating three different stages of technology development; (2) developing a heuristics to switch the signal controls depending on the stage of technology; (3) increasing the computational efficiency with a branch and bound solution method; (4) incorporating trajectory design for automated vehicles; (5) using a Kalman filter to reduce the impact of measurement errors on the final solution. Three categories of vehicles are considered in this paper to represent different stages of this technology: conventional vehicles, connected but non-automated vehicles (connected vehicles), and automated vehicles. The proposed algorithm finds the optimal departure sequence to minimize the total delay based on position information. Within each departure sequence, the algorithm finds the optimal trajectory of automated vehicles that reduces total delay. The optimal departure sequence and trajectories are obtained by a branch and bound method, which shows the potential of generalizing this algorithm to a complex intersection.Simulations are conducted for different total flows, demand ratios and penetration rates of each technology stage (i.e. proportion of each category of vehicles). This algorithm is compared to an actuated signal control algorithm to evaluate its performance. The simulation results show an evident decrease in the total number of stops and delay when using the connected vehicle algorithm for the tested scenarios with information level of as low as 50%. Robustness of this algorithm to different input parameters and measurement noises are also evaluated. Results show that the algorithm is more sensitive to the arrival pattern in high flow scenarios. Results also show that the algorithm works well with the measurement noises. Finally, the results are used to develop a heuristic to switch between the different control algorithms, according to the total demand and penetration rate of each technology.

Getting the Human Factor into Traffic Flow Models: New Open-Source Design to Simulate Next Generation of Traffic Operations

Automated driving may lead to much higher road capacities, combined with increased road safety, increased driver comfort, and lower costs. Although this vision may hold ground in the long run, first a transitional period will take place in which increasing percentages of vehicles with many levels of automation will drive on the world’s road networks. This transition poses a fundamental scientific challenge. The models used today to simulate and predict vehicular traffic are not valid to predict emergent properties of traffic flows under increasing amounts of vehicle automation. For example, there is no idea of how drivers of nonautomated vehicles will respond to other drivers reading their morning papers behind the steering wheel or the consequences of such interactions on traffic safety and capacity. In this paper, the authors do not propose a new behavioral theory with which the effects of increasing vehicle automation can be predicted. What the authors propose is an advanced open-source simulation framework, OpenTrafficSim, which makes it possible to extend microscopic models incrementally with explanatory mental models, such that new behavioral theories can be tested and shared within the community. Given the societal importance of predicting the effects on safety and efficiency of vehicle automation, the authors sincerely hope this paper will fuel the discussion on how both open-source and closed-source simulation software can be adapted and made ready for the next generation of traffic simulation models that are needed in the coming decades.

Investigation into the Role of Rational Ethics in Crashes of Automated Vehicles

Traffic engineers, vehicle manufacturers, technology groups, and government agencies are anticipating and preparing for the emergence of fully automated vehicles into the American transportation system. This new technology has the potential to revolutionize many aspects of transportation, particularly safety. However, fully automated vehicles may not create the truly crash-free environment predicted. One particular problem is crash assignment, especially between automated vehicles and nonautomated vehicles. Although some researchers have indicated that automated vehicles will need to be programmed with some sort of ethical system in order to make decisions on how to crash, few, if any, studies have been conducted on how particular ethical theories will actually make crash decisions and how these ethical paradigms will affect automated vehicle programming. The integration of three ethical theories—utilitarianism, respect for persons, and virtue ethics—with vehicle automation is examined, and a simple programming thought experiment is used to demonstrate the difficulty in selecting and implementing different ethical decisions. A simple crash scenario is introduced; an automated vehicle must choose between three crash types on the basis of a randomly assigned ethical theory. The results of the experiment indicate that in specific crash scenarios, utilitarian ethics may reduce the total number of fatalities that result from automated vehicle crashes, although other ethical systems may be useful for developing rules used in machine learning. The experiment demonstrates that understanding rational ethics is crucial for developing safe automated vehicles.

A simulation tool for automated platooning in mixed highway scenarios

Automated platooning is one of the most challenging fields in the domain of Intelligent Transportation Systems (ITS). Conceptually, platooning means creating clusters of vehicles which closely follow each other autonomously without action of the driver, neither for accelerating, nor for braking. This leads to several important benefits from substantially improved road throughput to increased safety. The control of such platoons depends on two components: First, radar is typically to be used to control the distance between the vehicles, and secondly, Inter-Vehicle Communication (IVC) helps managing the entire platoon allowing cars to join or to leave the group whenever necessary. Platooning systems have been mostly investigated in controlled environments such as dedicated highways with centralized management. However, platooning-enabled cars will be deployed gradually and might have to travel on highways together with other non-automated vehicles. We developed a combined traffic and network simulator for studying strategies and protocols needed for managing platoons in such mixed scenarios. We show the models needed and present first results using a simple IVC-based platoon management as a proof of concept.

Back to the future: Brake reaction times for manual and automated vehicles

Rear-end collisions are often quoted as being a major cause of road traffic accidents. In response to this, a great deal of ergonomics research effort has been directed towards the analysis of brake reaction times. However, the engineering solution has been to develop advanced systems for longitudinal control, which, it is argued, will mitigate the problem of rear-end collisions. So far, though, there have been few empirical studies to determine how brake reaction times will be affected by such vehicle automation. This paper presents a literature review summarizing the current state of knowledge about driver responses in non-automated vehicles. The review covers driver factors, vehicle factors and situational factors. Following the review, some empirical data are presented from a driving simulator experiment assessing brake reaction times of skilled and unskilled drivers under two different levels of automation. When compared to previous data gathered during manual driving, there seems to be a striking increase in reaction times for these automated conditions. Implications for the design and safety of automated vehicle systems are discussed.

Interaction of Automated and Non-Automated Vehicles

Novel car technologies like driver assistance systems may have unforeseen and unintended consequences on overall performance of the vehicle-driver-environment system. As an example the question about the interaction of vehicles with and without automatic support systems is discussed, for further investigations computer simulation of typical and possibly critical traffic situations, where both human conductors and support systems are involved, is recommended. For this purpose a model that accounts for the cognitive capabilities of human drivers is developed.

Advanced Vehicle Command and Control System (AVCCS)

This paper explores the research under way in developing an autonomous vehicle command and control system; it also explains the means by which guidance is maintained and in‐yehicle navigation system are integrated into the vehicle control system. This system integration feature allows the vehicle to operate in a mixture of both automatic and manually controlled vehicles. The unique features investigated in this research include the relationship between the on‐board control system and other nonautomated automobiles; the efficiency and usefulness of the new navigation concept; and the system approach to interactive linking of the in‐vehicle control system with three transportation modes: (1) Other automated vehicles; (2) other nonautomated vehicles; and (3) the public street and/or freeway control system. Further discussed, in system descriptive terms, is the actual in‐vehicle control system, in‐pavement transponders, a flux gate compass, an automatic vehicle location reader, and lateral/longitudinal radar proximity devices. The text also illustrates the logic of the on‐board control system including the development of the various signal conditioning devices and local slave computer systems required to drive the main management/navigational computer system.