Interaction-Aware Motion Planning as a Game

An AV-MV negotiation method based on synchronous prompt information on a multi-vehicle bottleneck road

The advent of automated vehicles (AVs) will provide opportunities for safer, smoother, and smarter road transportation. During the transition from the current human-driven vehicle (HV) to a fully AV traffic environment, there will be a mixed traffic flow including both HVs and AVs. The impact of introducing AVs into existing traffic, however, has not yet been fully understood. In this paper, we advance this understanding by conducting mixed traffic safety evaluation from the perspective of car-following behavior using real-world AV operational data of mixed traffic. To understand how the AVs impact other vehicles on the road, we analyzed the operational behaviors of HV-following-HV, AV-following-HV, and HV-following-AV. A selected car-following model is calibrated, and results show that there are significant differences between the HV-following-HV and the other two groups, indicating safe AV behavior and changes in HV behavior (i.e. less aggressive, safer) after the introduction of AVs into the traffic. Additionally, to understand AV behavioral safety, we investigate behavior predictions (one of the most critical inputs for AVs to make car-following decisions) of AVs and their surrounding vehicles using a mature baseline model and a new Conditional Variational Autoencoder (CVAE) framework. The result shows potential risks of inaccurate predictions of the baseline model and the necessity to consider additional factors, such as vehicle interactions and driver behavior, into the prediction for risk mitigation. Arterial vehicle trajectory data from the Lyft Level 5 Dataset is applied to test the proposed methodological framework to understand the car-following safety risks of HVs and AVs in the mixed traffic stream.

Analyzing the impact of automated vehicles on uncertainty and stability of the mixed traffic flow

The emergence of Automated Vehicles (AVs) will result in mixed traffic with Human-Driven Vehicles (HDVs), creating a complex traffic environment. While literature supports the effect of AVs on human driving behavior, the influence of different AV driving styles in this context remains largely unexplored. This study investigated interactions between HDVs and AVs varying in driving style (cautious vs. aggressive) and appearance (recognizable vs. unrecognizable) at Market Penetration Rates (MPR) of 0% to 75% in 25% increments within a highway context. Car-following behavior of 160 participants (56 females, age range 19-65) in a driving simulator experiment was analyzed using average time headway (THW) and standard deviation of relative speed (SDRS) as key metrics. Linear Mixed Models (LMMs) and Structural Equation Modelling (SEM) were applied. Results showed that compared to the baseline (no AVs), drivers' THW decreased when interacting with aggressive AVs by 11.1%, 8.6%, and 13.2% at MPRs of 25%, 50%, and 75%, respectively. Interactions with cautious AVs led to even larger reductions in THW by about 9.9%, 16.6%, and 17.3% at the same MPRs. SDRS improved across MPRs by 4.5%, 5.2%, and 6.2%, independent of AV driving style or appearance. SEM analysis indicated that AV driving style moderated relationships between independent, mediating, and dependent variables, while drivers' perceived stress and comfort mediated effects of AVs, trust propensity, and recognizability on THW and SDRS. These findings provide insights into human responses to AV behavior, highlighting implications for traffic flow and safety to ensure smoother and safer human-AV interactions.

Flexible manipulators are widely applied in many fields. Here, the control design for a simplified flexible manipulator model with nonlinear inputs and state constraints is studied. The impact of two inputs and disturbances on the system was considered. One torque input comes from the joint motor, and the other input force comes from the linkage actuator tip. The input constraints of a dead zone are applied to both inputs to the manipulator. To offset the effect of the nonlinear input, we first linearize the dead zone and convert it into a linear-input characteristic and a finite error value. Then, the adaptive rate is designed to compensate for the effects of the nonlinear input. For the state constraints, an adaptive controller is proposed based on a symmetric tangent-type barrier Lyapunov function which can operate under closer constraint conditions, and parameter tunability offers flexibility in balancing the constraints’ tightness and performance. The stability proof ensures that all states are within the given constraint range. The provided simulation results indicate that the system is not sensitive to the initial values, and when the initial values are taken to be between open intervals (−0.4, 0.34), this ensures the stability of the system and does not violate the constraint bounds.

Integration of automated vehicles in mixed traffic: Evaluating changes in performance of following human-driven vehicles

Vehicles with adaptive cruise control, i.e., SAE Levels 1 and 2 automated vehicles (AVs), have been operating on roads with a significant and rapidly growing penetration rate. Identifying these AVs is critical to understanding near-future mixed traffic characteristics and managing highway mobility and safety. This study identifies adaptive cruise control-equipped vehicles from human-driven vehicles (HVs) by constructing a set of learning-based models using car-following trajectories in a short time window. It is extendible to Level 3 and + AV identification when data is available. To compare model performance and draw physical insights, two physics-based models are proposed based on the premise that, in general, the car-following behavior of an AV is less volatile than an HV. Four car-following datasets, including AV makes from different manufacturers, are mixed to build a comprehensive identification model. Results show that physics-based approaches identify more than 80% AVs and 70% HVs. The identification accuracy of learning-based models is even higher. For example, the cluster-aware long short-term memory network identifies 98.79% of AVs and 95.45% of HVs. Learning-based identification models developed by this study can be integrated with the existing infrastructure (e.g., surveillance cameras), which have been used to extract car-following trajectories, to detect AVs in mixed traffic streams. This opens unparalleled data-driven opportunities to analyze and control mixed traffic to enhance safety (e.g., notifying surrounding traffic of the presence of AVs) and mobility (e.g., opening AV dedicated lanes when the percentage is great enough).

Automated vehicles (AVs) are expected to shape the future of transportation and to improve traffic flow and safety. Studies have focused on AVs effects on traffic flow during the transition to full automation, with few examining their influence on human-driven vehicles (HDVs). This study investigated potential changes in HDVs’ driving behavior induced by the presence of AVs with different driving styles (aggressive vs. cautious) at varying market penetration rates (MPRs) (0%, 25%, 50%, and 75%). First, a driving simulator experiment with 160 people (56 females, 104 males) was conducted to collect HDV trajectory data. Then, a microsimulation model was implemented in VISSIM, where HDV behavioral parameters were calibrated using the driving simulator data. Average time headway (THW), relative velocity (RelVel), average acceleration (Acc), average deceleration (Dec), and lane change frequency (LnCh) were used as behavioral metrics. A two-way ANOVA was applied for analysis. Results showed that higher AVs’ MPRs decreased THW, Acc, and Dec in HDVs, while RelVel increased with cautious AVs and decreased with aggressive AVs. Similar trends were observed for LnCh. These findings highlight the need to consider potential HDVs behavioral adaptation during the transition phase, as neglecting it may lead to inaccurate traffic assessments and ineffective policies.

Automated vehicles (AVs) are ready to share public roads with humans in mixed traffic. How human drivers will interact with AVs is unclear. We assumed that AVs’ aberrant behaviors (e.g., driving slowly) provoke higher driving anger than identical ones by human drivers do and examined this assumption from the perspective of mind perception (i.e., perception of whether AVs have human-like minds or lack thereof). Mind perception has two dimensions: agency (capable of doing and planning) and experience (capable of feeling and sensing). Our survey (N = 622) supported this assumption. Agency attribution to AVs is negatively correlated with driving anger, suggesting that perceiving AVs as lacking agency might be associated with greater anger toward AVs’ aberrant behaviors. Attributing high agency to AVs eliminated the human-AV difference. The relationship between driving anger and experience attribution was U-shaped: Participants attributing “middle” experience to AVs reported lower anger than those attributing no or “high” experience to AVs. We speculate that perceiving AV as having high capability to feel and sense might induce the “uncanny valley” effect documented in the literature on human-robot interaction, enhancing more negative reactions. Our findings suggest AVs’ aberrant behaviors and their induced anger as emerging risks to future mixed-traffic flow.

<div class="section abstract"><div class="htmlview paragraph">With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink software is established, providing a comprehensive environment for simulating AV behavior. Secondly, an evaluation system is developed to assess AV’s feasibility on longitudinal roads, based on safety indicators (rear-end collisions occurrence) and comfort indicators (axial acceleration, vertical acceleration, and vertical acceleration change rate). Lastly, the feasibility of ACC-AVs on existing longitudinal profile roads is simulated and evaluated. The results indicate that, on straight slope section, ACC-AVs may experience rear-end collisions on downhill sections with design speeds below 100 km/h; when safety requirements are met, both uphill and downhill sections exhibit good comfort levels. For the vertical curve section, comfort is also favorable in segments with low design speeds and large vertical curve radii, however, as curve radii decrease or design speeds increase, comfort deteriorates. The findings of this study provide a reference for optimizing highway profile design for AVs.</div></div>

Liability Design for Autonomous Vehicles and Human-Driven Vehicles: A Hierarchical Game-Theoretic Approach

After you?! – Use of external human-machine interfaces in road bottleneck scenarios

An agent-based urban simulation to assess the system-wide, spatial, and modal impacts of the deployment of an automated vehicle corridor with mixed traffic: a case study in New York City

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.

This article investigates the simultaneous interaction between an automated vehicle (AV) and its passenger, and between the same AV and a human driver of another vehicle. For this purpose, we have implemented a multi-vehicle simulation consisting of two driving simulators, one for the AV and one for the manual vehicle. The considered scenario is a road bottleneck with a double-parked vehicle either on one side of the road or on both sides of the road where an AV and a simultaneously oncoming human driver negotiate the right of way. The AV communicates to its passenger via the internal automation human–machine interface (HMI) and it concurrently displays the right of way to the human driver via an external HMI. In addition to the regular encounters, this paper analyzes the effect of an automation failure, where the AV first communicates to yield the right of way and then changes its strategy and passes through the bottleneck first despite oncoming traffic. The research questions the study aims to answer are what methods should be used for the implementation of multi-vehicle simulations with one AV, and if there is an added benefit of this multi-vehicle simulation compared to single-driver simulator studies. The results show an acceptable synchronicity for using traffic lights as basic synchronization and a distance control as the detail synchronization method. The participants had similar passing times in the multi-vehicle simulation compared to a previously conducted single-driver simulation. Moreover, there was a lower crash rate in the multi-vehicle simulation during the automation failure. Concluding the results, the proposed method seems to be an appropriate solution to implement multi-vehicle simulation with one AV. Additionally, multi-vehicle simulation offers a benefit if more than one human affects the interaction within a scenario.