Human-driven Vehicles Research Articles

Assessment of future parking systems with autonomous vehicles through agent-based simulation: A case study of Hangzhou, China

Autonomous vehicles (AVs) are expected to transform urban parking. AVs may not require dedicated parking spaces near their destination. Instead, AVs can drive to the optimal parking lot, cruise, or return to the origin after the passenger gets off. Previous research on AV parking strategies has largely overlooked actual trip patterns and road network conditions that can affect the effectiveness and feasibility of these strategies. In this paper, we construct an agent-based simulation model to assess the potential impacts of AVs on urban parking, focusing on new AV parking strategies. Using the real traffic network and trip data of Hangzhou, we assess the impacts on overall network efficiency, personal trip, and parking demand under different AV penetration rates. Results show that AVs could decrease the total parking cost from 23 to 6 RMB and increase parking convenience for both AVs and human-driven vehicles (HDVs). 21% of AVs will still choose to park, while 70% will return to the origin. On average, each trip with AV will generate an additional zero-occupant distance of 8.8 km, exacerbating the congestion. The findings indicate the benefits and challenges of new AV parking strategies, providing valuable insights for future urban parking planning and policy making.

A mixed traffic car-following behavior model

With the advancement of communication and autonomous driving technologies, a mixed traffic flow comprising human-driven vehicles (HVs), connected human-driven vehicles (CHVs), and connected autonomous vehicles (CAVs) is emerging. In this paper, we propose a generalized car-following model for mixed traffic flow, which considers both the human drivers’ characteristics (i.e., perception ability of distance, acceleration, and speed, trust level in connected vehicle information, and driving style) and information from multiple leading connected vehicles (CVs). Through numerical experiments, we analyze the influences of mixed traffic flow composition schemes, communication distance, and human drivers’ characteristics on mixed traffic flow. The results show that the proposed model can effectively capture the car-following behavior of different types of vehicles in mixed traffic flow. The contribution of CHVs to mixed traffic flow stability is significantly less than that of CAVs due to the involvement of human drivers’ characteristics. Human drivers’ characteristics significantly influence average fuel consumption (FC) within mixed traffic flow. Specifically, inaccurate perception of distance by human drivers can lead to an increase in the average FC, while a higher level of trust in connected information leads to lower average FC. Furthermore, the results reveal that the communication distance between CVs plays a pivotal role in the stability of mixed traffic flow.

A deep reinforcement learning-based approach for autonomous lane-changing velocity control in mixed flow of vehicle group level

As an important driving behavior, lane-changing has a great impact on the safety and efficiency of traffic flow interacting with surrounding vehicles, especially in mixed traffic flows with autonomous vehicles and human-driven vehicles. This study proposes a deep reinforcement learning-based lane-changing model to train autonomous vehicles to complete lane-changing in the interaction with different human driving behaviors. First, a mixed-flow lane-changing environment of vehicle group level is constructed with surrounding vehicle trajectories extracted from natural driving trajectories. Then, the state space and action space are determined, the reward function is designed to comprehensively consider safety and efficiency, so as to guide autonomous vehicles not to collide, and determine the acceleration and direction angle to complete lane-changing behavior, and a collision avoidance strategy is integrated into the proposed method to ensure the safety of longitudinal motion. Furthermore, the trained model can learn the experience of successful lane-changing, resulting in a 90% success rate without collision in testing. Finally, the driving performance of the proposed method is analyzed in terms of safety and efficiency evaluation indicators, which proves that the proposed method can improve the efficiency and safety of the lane-changing process.

Pavement rutting performance analysis of automated vehicles: contribution of flexible pavement layers’ characteristics

Designing and implementing appropriate pavement structures in the era of coexisting automated vehicles (AVs) and human-driven vehicles (HDVs) is a significant concern for researchers and road practitioners. This necessitates investigating the pavement performance subjected to the various loading distribution scenarios induced by different lateral movement patterns of AVs. It is known that the pavement layers' thickness and material influence pavement performance. This study used a finite element method (FEM) to model AVs' loading distributions and simulate the rutting performance of a full-depth flexible pavement structure constructed in different layers' thicknesses and materials. The results revealed that the layers' thickness and material contribution depend on the lane distribution scenario and the AVs' wander mode. In some AV scenarios, improving the layers' material and increasing the thickness could help reduce the pavement rutting damage compared to HDV scenarios, with the extent of this effect influenced by AVs' wander mode.

Mixed traffic system with multiple vehicle types and autonomous vehicle platoon: Modeling, stability analysis and control strategy

This paper focuses on platoon control of autonomous vehicles (AVs) equipped with V2X technology and the phenomenon that the large platoons are split into multiple sub-platoons due to the communication restrictions. At the same time, with the popularity of AVs and connected human-driven vehicles (CVs), this paper also carries out the research of mixed traffic flow mixed with AV platoons, CVs and regular human-driving vehicles (RVs). According to the characteristics of the different types of vehicles, this paper extends the corresponding car-following models based on intelligent driver model (IDM), and considers the functional degradation of AVs under different car-following modes. In this paper, the AVs are classified into the second-level degenerated AV (D2AV), the first-level degenerated AV (D1AV) and the normal functioning AV (NAV) based on the differences in car-following modes. The degradation of AVs is represented via the change of communication topologies and models. Linear stability analysis is conducted. Numerical simulation shows that the higher level of AV degradation, the worse the stability of traffic flow. Under the same conditions, the more sub-platoons and the larger maximum size of sub-platoons, the better the stability of the traffic flow. The increase in communication strength of AVs is also beneficial to stability of traffic flow. In order to compensate for degradation, an adaptive gain feedback control strategy is proposed. Simulation shows that the adaptive gain feedback strategy can effectively improve the ability of the traffic flow to resist disturbances, reduce the fluctuations of velocity and acceleration, and contribute to the stability of platoon system and traffic flow.

A comparison of pedestrian behavior in interactions with autonomous and human-driven vehicles: An extreme value theory approach

Autonomous Vehicle (AV) technologies are expected to result in significant safety and mobility benefits to the road system. However, one of the most important issues that autonomous vehicle technology faces is ensuring safe interactions with active road users such as pedestrians who can have unpredictable behavior. Moreover, road user behavior varies considerably across different traffic environments, which might represent a challenge to implementing AVs as they lack the intuition common in human-driven vehicles (HDV). This study proposes an approach to evaluate crash risk in vehicle–pedestrian interactions. An Extreme Value Theory (EVT) Peak Over Threshold (POT) model is used to compare the crash risk of AV-pedestrian and HDV-pedestrian interactions in four different cities, namely Boston, Las Vegas, Pittsburgh, and Singapore. A Bayesian hierarchical structure is used to incorporate the effect of several behavioral covariates, which enables estimating the crash risk of each interaction. Results show that the risk varies considerably depending on the type of interaction and the environment. For example, the impact of behavioral covariates (i.e., minimum distance between road users and maximum pedestrian speed) on the risk of AV-pedestrian interactions is greater when compared to the risk of HDV-pedestrian interactions in Boston, Las Vegas, and Singapore. This might indicate that, in busy and congested environments, road users may not be entirely comfortable with the presence of AVs. In addition, Singapore presented a higher percentage of riskier AV-pedestrian interactions when compared to the other cities. Finally, this study offers significant insights into the challenges of introducing AVs in diverse environments as behavior plays a crucial role in traffic and can influence conflict occurrence.

Stability analysis and connected vehicles management for mixed traffic flow with platoons of connected automated vehicles

Recently, the market has witnessed the emergence of intelligent vehicles equipped with diverse functionalities. Among these are connected automated vehicles (CAVs) boasting a comprehensive platooning capability, and connected vehicles (CVs), which essentially refer to human-driven vehicles (HVs) equipped with connectivity features. As a result, CAVs, CVs, and regular human-driven vehicles (RVs) will form a mixed CAV-CV-RV flow to affect stability of existing traffic flow, which is an important characteristic of traffic flow operations. This paper investigates stability of this new mixed CAV-CV-RV flow, explores effect of CVs on stability, and proposes CV management strategy aimed at achieving a stable traffic flow. We firstly establish definition of the novel mixed flow comprising CAVs, CVs, and RVs. Subsequently, we present a general analytical approach for evaluating stability of this mixed flow. Then we undertake an empirical study that incorporates car-following models, validated using real experiments involving the aforementioned vehicle types. Based on findings from our stability analysis, we proceed to propose a CV management strategy designed to guarantee stable conditions within this mixed traffic flow. Results indicate the effectiveness of the proposed general analytical method in assessing influences of various system factors on stability of this new mixed flow. Subsequent to an application of the analytical method, empirical study uncovers a noteworthy finding that CVs prove to be more effective than CAVs in ensuring stable conditions within the mixed flow. Then we can manage market proportion of CVs in order to achieve stable mixed flow, according to varying CAV penetration rates. Specifically, some interesting lessons are learned based on the empirical study: (i) to guarantee stable operations of mixed traffic flow, about one third of CVs are needed to be among HVs, before CAVs running on road (CV-RV mixed flow); (ii) management strategy for stable flow requires an increase of minimum CV proportion among HVs from 33% to 55%, as CAV penetration rates increase up to 50%; (iii) when CAV penetration rates are greater than 50%, the required minimum CV proportion for ensuring traffic flow stability decreases with the increase of CAV penetration rates.

Managing mixed traffic at signalized intersections: An adaptive signal control and CAV coordination system based on deep reinforcement learning

Managing the mixed traffic involving connected and autonomous vehicles (CAVs) and human-driven vehicles (HVs) at a signalized intersection has become a concern of researchers. However, the performances of most existing control methods are limited, especially when CAV penetration rate is low, since they fail to make a better trade-off between safety and operational efficiency for both CAVs and HVs. To this end, this study proposes a deep reinforcement learning (DRL) powered control system for the mixed traffic at signalized intersections, which aims to optimize operational efficiency of both CAVs and HVs while assuring safety and reducing interference on HVs’ driving habits. The system adopts an adaptive traffic signal control strategy and an efficient CAV control policy with a passing rule proposed as a link in between. The traffic signal control strategy allows traffic light to adaptively adjust its phase and duration based on real-time traffic information, while the CAV control policy permits the CAVs meeting certain safety constraints to form platoons and pass through the intersection in a coordinated manner regardless of traffic signals. As an efficient DRL algorithm, Deep Q-Network (DQN) is adopted to adaptively control the signals and implement CAV coordination. The proposed system is examined on Simulation of Urban Mobility (SUMO), given different CAV penetration rates and traffic conditions. It is found that the proposed system not only outperforms the state-of-the-art control methods on reducing travel time and fuel consumption under low CAV penetration rate, but also enlarges its advantages with the increase of CAV penetration rate. In certain traffic scenarios, the proposed system can even achieve a maximum reduction of travel time by 37.33% and fuel consumption by 15.95%, in comparison to the existing method with the best performance. Besides, to some extent, the comparisons between the performances of CAVs and HVs demonstrate certain benefits of introducing CAVs into the mixed traffic.

Connected and automated vehicle loading system for improving operational inefficiency from human driven vehicle in roll-on/roll-off port operations

ABSTRACT This study aims to identify the non-value-adding activities during vehicle stowage operations in automobile terminals and propose a connected automated vehicle (CAV) loading system, a self-driving-car-loading system. Furthermore, the productivity of the CAV loading system is compared with the current loading system. A simulation model of an actual loading system was developed using the software FlexSim. The simulation results showed that the walking time of workers, operation time of shuttle vans, and waiting time occupied a large part of the cycle time in the current operation system. The proposed CAV loading system has eliminated these inefficiencies, and increased productivity by 26.78%. This is the first study to (1) present a self-driving-car-loading system in a simulated automobile terminal of a real-world size, and (2) propose a CAV loading system. Results provide useful insights for the integration of self-driving technology into future automobile port operations.

Graph Reinforcement Learning-Based Decision-Making Technology for Connected and Autonomous Vehicles: Framework, Review, and Future Trends.

The proper functioning of connected and autonomous vehicles (CAVs) is crucial for the safety and efficiency of future intelligent transport systems. Meanwhile, transitioning to fully autonomous driving requires a long period of mixed autonomy traffic, including both CAVs and human-driven vehicles. Thus, collaborative decision-making technology for CAVs is essential to generate appropriate driving behaviors to enhance the safety and efficiency of mixed autonomy traffic. In recent years, deep reinforcement learning (DRL) methods have become an efficient way in solving decision-making problems. However, with the development of computing technology, graph reinforcement learning (GRL) methods have gradually demonstrated the large potential to further improve the decision-making performance of CAVs, especially in the area of accurately representing the mutual effects of vehicles and modeling dynamic traffic environments. To facilitate the development of GRL-based methods for autonomous driving, this paper proposes a review of GRL-based methods for the decision-making technologies of CAVs. Firstly, a generic GRL framework is proposed in the beginning to gain an overall understanding of the decision-making technology. Then, the GRL-based decision-making technologies are reviewed from the perspective of the construction methods of mixed autonomy traffic, methods for graph representation of the driving environment, and related works about graph neural networks (GNN) and DRL in the field of decision-making for autonomous driving. Moreover, validation methods are summarized to provide an efficient way to verify the performance of decision-making methods. Finally, challenges and future research directions of GRL-based decision-making methods are summarized.

Brain-Inspired Modeling and Decision-Making for Human-Like Autonomous Driving in Mixed Traffic Environment

In this paper, a human-like driving system is designed for autonomous vehicles (AVs), which aims to make AVs better integrate into the human transportation systems and mitigate misunderstanding and conflicts when interacting with human-driven vehicles. Based on the analysis of the real world INTERACTION dataset, a driving aggressiveness estimation model is established with the fuzzy inference approach. In the human-like lane-change decision-making algorithm, the cost function is designed comprehensively considering driving safety and travel efficiency. Based on the cost function with multi-constraint, a dynamic game algorithm is developed to model the interactions and decision making between AV and human-driven vehicles. Additionally, to guarantee the safety during lane-change of AVs, an artificial potential field model is built for collision risk assessment. Further, a human-like driving model is designed, which integrates the brain emotional learning circuit model (BELCM) with a two-point preview model. Finally, the proposed algorithm is evaluated through human-in-the-loop experiments, and the results demonstrated the feasibility and effectiveness of the proposed method.

Coupling Control of Traffic Signal and Entry Lane at Isolated Intersections Under the Mixed-Autonomy Traffic Environment

There is a growing number of studies on the traffic control strategies of signal timings and vehicle trajectories at signalized intersections, while lane assignments are widely pre-specified and fixed. Meanwhile, existing strategies generally require a fully connected and automated vehicles (CAVs) environment. To fill up the gaps, this study contributes to a two-dimensional (spatiotemporal) control strategy by jointly optimizing traffic signals, lane settings, and vehicle trajectories at isolated signalized intersections under the mixed traffic of connected automated and human-driven vehicles. Specifically, based on the pseudo-platoons, signal timing plans and settings of approach lanes are jointly optimized by a piece-wise linear programming model. Then, vehicle trajectory control is integrated into the collaborative control framework to smooth vehicle trajectories. Three groups of numerical experiments are conducted to verify the effectiveness and efficiency of the proposed control method. Results show that the proposed algorithm outperforms the actuated control in terms of vehicle travel time under both under-saturated and over-saturated traffic conditions.

Centralized Model Predictive Control With Human-Driver Interaction for Platooning

Cooperative adaptive cruise control presents an opportunity to improve road transportation through increase in road capacity and reduction in energy use and accidents. Clever design of control algorithms and communication systems is required to ensure that the vehicle platoon is stable and meets desired safety requirements. In this paper, we propose a centralized model predictive controller for a heterogeneous platoon of vehicles to reach a desired platoon velocity and individual inter-vehicle distances with driver-selected headway time. As a novel concept, we allow for interruption from a human driver in the platoon that temporarily takes control of their vehicle with the assumption that the driver will, at minimum, obey legal velocity limits and the physical performance constraints of their vehicle. The finite horizon cost function of our proposed platoon controller is inspired from the infinite horizon design. To the best of our knowledge, this is the first platoon controller that integrates human-driven vehicles. We illustrate the performance of our proposed design with a numerical study, demonstrating that the safety distance, velocity, and actuation constraints are obeyed. Additionally, in simulation we illustrate a key property of string stability where the impact of a disturbance is reduced through the platoon.

Cooperative On-Ramp Merging Control Model for Mixed Traffic on Multi-Lane Freeways

This paper proposes a hierarchical model for cooperative on-ramp merging control (CORMC) in mixed traffic with both connected automated vehicles (CAVs) and connected human-driven vehicles (CHVs). The upper-layer of the CORMC model employs an anticipatory position searching (APS) algorithm to determine the anticipatory positions at which merging vehicles (MVs) should merge from the on-ramp lane to the adjacent mainline lane, and to assign cooperative vehicles (CVs) for each MV. A collaborative utility choice (CUC) model is presented to determine the optimal maneuver of CVs to create proper gaps for MVs. The driver compliance rate is introduced to account for CHVs’ unwillingness to follow the instructions given by the CUC model. The lower-layer comprises a cooperative merging control (CMC) model that ensures safe and smooth merging execution for MVs. Longitudinal and lane changing models are developed for mainline vehicles to facilitate an efficient and safe merging process. Simulation results show that the performance benefits of the CUC model are marginal when the CHV compliance rate is relatively low. However, the performance improvement is significant at higher compliance rates ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$&gt;$</tex-math> </inline-formula> 50%). Furthermore, the CORMC model has the potential to increase merging and mainline throughput by over 75% at sufficiently high CAV penetration rates. Comparison of three control strategies shows that the APS algorithm plays an important role in the CORMC model. A comparison with the Simulation of Urban Mobility (SUMO) indicates that the CORMC model significantly mitigates the propagation of congestion waves across varying levels of CAV penetration and on-ramp flow rates.

Sustainable deployment of autonomous vehicles dedicated lanes in urban traffic networks

Autonomous vehicles (AVs) show promise for increasing roadway safety and capacity. During the AV transition era, which will be characterized by a mixed fleet of AVs and human-driven vehicles (HDVs), it is expected that the allure of these prospective benefits will motivate road agencies to allocate AV-dedicated lanes. This paper proposes a sustainability-driven AV-dedicated lane and pricing policy (SALP) framework that addresses the three pillars of sustainable development—social, environmental, and economic. The framework is formulated as a bi-level problem where the upper-level model yields decisions on the timing, location, and quantity of AV-dedicated lanes and tolling levels to minimize total travel time, emissions, and electricity consumption costs (that is, the economic and environmental pillars). To alleviate potential inequity (the social pillar), two considerations are proposed: revenue neutrality to compensate for the increase in travel costs of travelers and an equity constraint to limit the exacerbation of HDV travel costs. At the lower level, travelers react to the decisions made at the upper level by choosing their vehicle types (AV vs. HDV) and routes. The SALP is solved using Genetic and Frank-Wolfe algorithms. The results of the numerical experiments suggest that the proposed SALP addresses all three pillars, as it yields significant reductions in total travel time, emissions, and electricity costs.

Modeling and analysis of car-following models incorporating multiple lead vehicles and acceleration information in heterogeneous traffic flow

In the foreseeable future, a complex heterogeneous traffic environment will emerge as Connected and Autonomous Vehicles (CAVs) coexist with Human-driven Vehicles (HDVs). Consequently, understanding the impact of CAVs on car-following behavior and the operational characteristics of heterogeneous traffic flow becomes crucial before their widespread deployment. To tackle this challenge, this research proposes an improved car-following model based on the Intelligent Driver Model (IDM). The model incorporates the position, velocity, and acceleration information of both front and sub-front vehicles in the heterogeneous traffic flow. The impact of different types of information on the model's stability is verified through linear stability analysis while investigating the operational characteristics of traffic flow during vehicle start-up. Additionally, the car-following modes are classified based on the type of leading vehicle, and the corresponding following model is formulated. The results indicate that the improved model significantly improves traffic flow stability, particularly when considering acceleration information. Compared to the front vehicle, the influence of the sub-front vehicle on traffic flow stability is less significant, but their combined impact yields positive effects. Furthermore, the improved model reduces the start-up time of vehicles at signalized intersections by 7.9% and enables a smoother start-up process for vehicles. Moreover, CAVs can mitigate the impact of HDV's disturbances on the overall heterogeneous fleet operation by adjusting their spacing relative to the front vehicle. With an increasing penetration rate, the velocity fluctuation of the entire fleet decreases.

Modeling the Effect of the Platoon Size of CAVs on Mixed Traffic Flow: A Cellular Automaton Method

This study proposes a cellular automaton model incorporating the platoon size of connected automated vehicles (CAVs) to examine their impact on mixed traffic flow. First, vehicles are classified into three modes, human-driven vehicles (HDVs), adaptive cruise control (ACC), and cooperative adaptive cruise control (CACC), by considering the characteristics of the car-following behavior. Second, the CACC is further subdivided into interplatoon and intraplatoon car-following modes due to the limitations of the platoon size of CAVs. Then, cellular automaton rules are developed for each of these four modes. Finally, numerical simulation experiments are conducted to analyze the influence of the penetration rate and platoon size of CAVs on mixed traffic flow. The results demonstrate that (1) the simulation results closely align with the theoretically derived outcomes, with an error rate of only 0.46% at a penetration rate of 100%; (2) when the penetration rate of CAVs reaches 100%, increasing the platoon size further enhances the traffic capacity; and (3) the optimal platoon size is determined to be seven CAVs under moderate traffic density.

Driving safety field modeling focused on heterogeneous traffic flows and cooperative control strategy in highway merging zone

Connected and Automated Vehicles (CAVs) will coexist with Human-Driven Vehicles (HDVs) in the foreseeable future. Proactive safe controls indeed play a vital role in thus a complex heterogeneous traffic environment. This paper mainly constructed the safety field models for vehicles and environments respectively to address the problems of unclear rules of heterogeneous traffic flow and unclear safety mechanisms in the complex scenes of merging areas. Firstly, the action range of CAV vehicle field was constrained with the differential approximation algorithm to fill in the weakness of the infinite influence range of the existed vehicle field. Then the safety field model of HDV was also reconstructed by taking into account the driver’s environmental psychological tolerance with three indicators from driver experience, environmental visibility and trust. Meanwhile, the environmental field model was innovatively set up with the longitudinal distance variable to overcome the drawbacks of sudden changes in field strength as the number of lanes changes. Based on above mechanism models, this study further reconstructed the early lane-changing model that cored with the minimum distance model and the mandatory lane-changing model in the merging zone that cored with the driving safety index (DSI). Those models provided explicit judgment for the selection of vehicle lane-changing model and CAV behavior control in the merging zone. Finally, the simulation experiments show that the cooperative control strategy with a higher of 45% CAV penetration rate in low-density traffic (1200 veh/h) scenario outperforms than those strategies of SUMO or without early lane-changing in adapting the features of highway merge zone. While in a high-density traffic (1600 veh/h) scenario that with a fixed 30% CAV penetration rate, the time-average speed and space-average speed of the cooperative control strategy improved 15.56% and 25.15%, respectively.

Eco-driving strategy for connected vehicles at signalized intersections considering human driver error

In recent years, eco-driving strategies based on connected vehicle (CV) technologies have been studied to assist human drivers to reduce fuel consumption and pollutant emissions. In this paper, a real-time eco-driving strategy for CVs that considers human driver error is proposed to improve both traffic and fuel efficiency at signalized intersections where CVs and human-driven vehicles (HDVs) coexist. Firstly, a human driver error estimation model is established using real-world driving data. Then, based on the signal phase and timing information, vehicle state information, and the estimated human driver errors, a constrained nonlinear optimal control problem (OCP) is proposed to calculate the optimal advisory speed of each CV. The trajectory of HDV is estimated by utilizing the Gipps’ car-following model. Fast stochastic model predictive control (SMPC) is employed to solve the proposed OCP effectively. At last, simulation studies and real-vehicle experiments are conducted in various scenarios to verify the performance of the proposed strategy. Simulation and experiment results indicate that compared with the baseline strategies, the proposed eco-driving strategy can significantly reduce travel time and fuel consumption while ensuring the real-time performance.

A generalized driving risk assessment on high-speed highways using field theory

This study presents a new safety measure derived from field theory. It evaluates the risk arising from the various concurrent conflicts within a platoon that can occur on high-speed highway driving situations, such as car-following, yielding, and lane changing. We defined the risk field as a finite scalar field produced by traveling vehicles on the road, and we defined the conflict field as the overlapping risk field between any vehicles in proximity on the roadway. The study used a probabilistic trajectory prediction model to construct risk fields and an approximation method to reduce the computational time for real-time applications. To demonstrate the applicability of the proposed new measure, we applied it to real-world trajectory data (NGSIM data from US Highway 101). We compared the results with three traditional conflict-based safety measures: post-encroachment time (PET), modified time-to-collision (MTTC), and deceleration rate to avoid a crash (DRAC). The new measure produced seamless and continuous risk estimations even during time windows when the other measures could not estimate the risk between vehicles. This is a major advantage over traditional measures. The study also developed visual displays of the estimated conflict fields to provide safety analysts with an intuitive and fast understanding of the results of the safety assessments made using the conflict field measure. We conclude that the proposed new safety measure provides a robust, reliable, and improved assessment of the risk involved in expected future mixed-traffic environments that involve both human-driven vehicles and automated vehicles in the future.