Microscopic traffic simulation models are promising for modeling detailed traffic features from the individual vehicle level, but they present challenges in optimization due to their computationally time-consuming, and non-analytic nature. This study addresses the difficulty of incorporating microscopic simulations into the optimization framework for multiple types of Active Traffic Management (ATM) strategies design by adopting the Simulation-Based Optimization (SBO) paradigm. A novel discrete SBO algorithm for highway ATM strategies designs, embedded within a Cell Transmission Model (CTM)-based meta model and a problem-specific Adaptive Hyperbox Algorithm (AHA) is proposed. The effectiveness of the proposed SBO method is validated through two cases: a toy highway and a real-world highway. The method is benchmarked versus the commonly utilized general-purpose model based-SBO algorithm. The experiments indicate that the proposed problem specific SBO algorithm outperforms the general-purpose model-based algorithms to obtain promising solutions in ATM strategies design problems, especially when the simulation resources are tight. Under ATM strategies derived by the proposed algorithm, the observed total delay on the highway shows a substantial 70% decrease. The ATM strategy obtained through the microscopic model-based optimization is proven to be superior to the macroscopic model-based approach. The introduced SBO algorithm in this work advances the current SBO theoretical framework by enhancing its applicability to highway traffic issues and overcoming obstacles related to the optimization of discrete variables.

The application of external human–machine interface (EHMI) on autonomous vehicles (AVs) facilitates information exchange. Existing research fails to consider the impact of the sequence of actions, as well as the effects of EHMI applications and deception, raising the question of whether benevolent, well-intentioned deception should be permitted (i.e., misleading statements that are intended to benefit both parties). We established a game theory based EHMI information disclosure framework for AVs in this study. In considering benevolent deception, this framework divided the decision-making process into three stages, respectively encompassing three key questions: whether to disclose, when to disclose, and what type of intention information to disclose. The results show that theoretical advantages of deception exist in certain cases when AV expects to maximize the safety of the interaction. In 40 out of 484 cases (8.3%), safety can be enhanced through successful deception. Those successful deceptions fall into two categories: 1) In 28 of these cases, the straight-going AV expected the left-turning human-driven vehicle (HV) to yield, while HV exhibited lower speed and higher acceleration; 2) In 12 of these cases, AV expected HV to proceed first, while HV exhibited higher speed and lower acceleration. We also conducted a VR-based driving simulation experiment, and the results confirmed our conclusion. Additionally, we found that when participants had low trust in the EHMI, its use negatively impacted interaction efficiency instead. This study serves as an exploratory behavioral mechanism study based on specific hypotheses for future EHMI design and ethical decision-making of autonomous driving system.

Advancements in Autonomous Vehicles (AVs) technology necessitate Active Traffic Management (ATM) strategies that incorporate fine-grained microscopic traffic features, accounting for AVs' distinct driving behaviors, decision-making patterns, and the specific road geometry design required for increasing AV penetration. While microscopic traffic simulation models are well-suited for capturing these microscopic features, their high computational demands and non-analytic nature have confined their use to ATM strategies optimization. To bridge this gap, this study employs a Simulation-Based Optimization (SBO) paradigm to integrate microscopic models into the design of ATM strategies for highways. We introduce an SBO framework that combines a Cell Transmission Model (CTM)-based meta model with the Adaptive Hyperbox Algorithm (AHA), where the CTM-based meta model approximates microscopic traffic conditions, and the AHA explores high-dimensional, discrete decision spaces to determine optimal ATM strategies. Our experiments demonstrate that the proposed SBO frame-work substantially outperforms other general-purpose SBO methods. This work advances ATM optimization by facilitating the integration of microscopic simulation models into practical optimization frameworks, ensuring the consideration of evolving traffic characteristics with the increasing presence of AVs.

Highly Automated Vehicles (HAVs) present complex challenges for regulators and industry stakeholders due to their susceptibility to diverse faults and the risks posed by their black-box nature during real-world operations. Regulators play a critical role in ensuring the safety and reliability of HAV systems, which necessitates robust fault detection and oversight mechanisms. A critical regulatory task is to define a minimal yet sufficient set of data (i.e., a Minimal Grey Box) that regulators should collect to trace the causal chain of any collision or near-collision events. However, conventional approaches, such as Event Data Recorders (EDRs) and Data Storage Systems for Automated Driving (DSSAD), fall short in detecting HAV -specific faults, mainly due to the vast volume of data generated by these systems. Furthermore, existing fault detection methods are often inadequate in identifying software-related issues, largely because of the opaque, black-box characteristics of HAVs. To address the challenges posed by the sheer size of the datasets and the opacity of the systems under oversight, this study introduces a fault detection method that extracts a Minimal Grey Box (MGB). The proposed approach first learns the causal structure of HAV faults using Bayesian Network, which are subsequently mapped onto a fault tree. MGB variables are then identified from the Minimal Cut Sets (MCS) of the fault tree, providing a streamlined input for fault detection. In a case study using the Baidu Apollo as the system under oversight, 18 key variables were extracted from an initial set of 79 to construct the MGB, resulting in a 68.4% reduction in data storage. The fault detection results demonstrate the superior performance of the detection model based on the concise MGB, achieving high F1-scores (>95%) across 5 Apollo sub-modules. This approach highlights a promising solution to the efficient oversight and accurate fault detection in HAVs.

Scenario-based testing has become a crucial method for certifying the safety of autonomous vehicles amidst the rapid advancement of autonomous driving technology. The exploration of worst-case scenarios, which plays a vital role in safety assessments and accelerated improvements of autonomous driving systems, has posed challenges due to the high dimensionality of parameters and the uncertainty of the decisions of autonomous driving systems. To address these challenges, a Bayesian optimization framework was proposed herein as a solution. Surrogate models, including two Gaussian process-based and five non-Gaussian process-based models, are employed to replace part of the costly simulation models, to overcome the challenge of high dimensionality. The Jackknife and Bootstrap methods are used to estimate variance and calculate the acquisition function. Additionally, a differential evolutionary algorithm is employed to achieve efficient solutions while balancing exploration and exploitation, to overcome the challenge of output uncertainty. The proposed framework is validated through experimental results. It successfully identifies the worst-case scenarios in all three cases and the average number of tests for the optimal model in the most complex 9-dimensional scenario is less than 400. Notably, the approach using the Random Forest (RF) surrogate model achieves the highest search success rate in all three cases.

Ensuring and improving the safety of autonomous driving systems (ADS) is crucial for deployment of highly automated vehicles, especially in safety-critical events. To address the rarity issue, adversarial scenario generation methods are developed, in which behaviors of traffic participants are manipulated to induce safety-critical events. However, existing methods still face two limitations. First, identification of the adversarial participant directly impacts the effectiveness of the generation. However, complexity of real-world scenarios, with numerous participants and diverse behaviors, makes identification challenging. Second, potential of generated safety-critical scenarios to continuously improve ADS performance remains underexplored. To address these issues, we propose LLM-attacker: a closed-loop adversarial scenario generation framework leveraging large language models (LLMs). Specifically, multiple LLM agents are designed and coordinated to identify optimal attackers. Then, the trajectories of attackers are optimized to generate adversarial scenarios. These scenarios are iteratively refined based on the performance of ADS, forming a feedback loop to improve ADS. Experimental results show that LLM-attacker can create more dangerous scenarios than other methods, and the ADS trained with it achieves a collision rate half that of training with normal scenarios. This indicates the ability of LLM-attacker to test and enhance the safety and robustness of ADS. The framework’s closed-loop design enables continuous scenario evolution compliant with regulatory standards, supporting both safety assurance and policy verification for ADS. Video demonstrations are provided at: https://drive.google.com/file/d/15rROV_ 8LUcc2jXSuSNBHVOHMCFKSn__B/view

With the rise of autonomous driving technology, Autonomous Vehicles (AVs) are becoming increasingly prevalent on public roads, influencing traffic conditions and the operation of Manual Vehicles (MVs). It is anticipated that a mixed traffic flow comprising both AVs and MVs will persist for an extended period. Current research concerning the impact of AVs often lacks a comprehensive focus on network-level effects and presents varying perspectives on network performance. Some studies have shown that AVs have a smaller headway and are therefore able to improve road capacity and reduce energy consumption and emissions. Some other scholars have found that existing commercial AVs may generate and spread oscillations, which can aggravate congestion in bottleneck areas if no coordination strategy is implemented. Such discrepancies may stem from differing application scenarios, datasets, and assumptions utilized in each study. Furthermore, these investigations have predominantly examined individual vehicles or fleets within localized road networks, such as single bottlenecks or small-scale networks, thus offering limited guidance at a macroscopic level. This paper seeks to elucidate the impact of AVs on macroscopic traffic patterns, particularly within mixed traffic scenarios, through simulation experiments conducted on both a grid network and a real-world network in Beijing.

Autonomous driving system is rapidly advancing, resulting in more Highly Automated Vehicles (HAVs) interacting with human-driven vehicles. In order to ensure safety during interaction, External Human-Machine Interface (EHMI) serves as a channel to display HAVs’ status and intentions. However, from the perspective of game theory, the selfish act of disclosing false information is detrimental to the advancement of HAV in most cases. It raises the question of whether certain acts of altruistic deception should be permitted if they can enhance the benefit for everyone in some certain cases. Therefore, it is worthwhile to investigate strategies for EHMI information disclosure. In this study, we establish a game-theoretic model of unprotected left-turn intersection scenarios. The impact of EHMI information disclosure is taken into consideration. The proposed model effectively achieves the simultaneous maximization of safety and efficiency during the human-machine interaction. In theory, altruistic deception may have benefits in certain scenarios, but from a practical standpoint, it is detrimental to the development of HAVs in the long run. Therefore, we recommend disclosing authentic information. By examining the benefits and potential drawbacks of altruistic deception, as well as considering the ethical considerations and legal implications, this study aims to contribute to the ongoing discourse on the ethical framework governing autonomous driving system.

Autonomous vehicles (AV) are consistently criticized for their inadequacies in harmoniously interacting with human-driven vehicles (HV), primarily attributed to the lack of sociality, a key human trait that balances individual and group rewards. Understanding sociality is essential for smooth AV navigation but remains challenging. To address this, we propose a Game-Theoretic Inverse Reinforcement Learning (GT-IRL) approach to quantify individualized sociality in driving interaction. Our approach identifies the sociality-preference parameters of a pre-designed reward function that integrates the ego agent’s rewards and the group rewards shared by all the interacting agents. Instead of presuming an agent is propelled by the maximization of its own rewards, the game-theoretical mechanism is utilized within the IRL structure to capture the fact that human drivers take into account others’ interests. We validated our method using human driving data from an unprotected left-turn scenario. The results demonstrate that the proposed GT-IRL outperforms state-of-the-art methods in better reproducing the evolution of the left-turn interaction at both semantic and trajectory levels. Additionally, cross-dataset analysis reveals variations in sociality due to geographical differences (China vs. the U.S.) and the nature of the interacting entities (AV vs. HV or HV vs. HV).

To validate whether Highly Automated Vehicles (HAVs) can live up to human expectations, it is essential to estimate their risk rate within the context of the naturalistic driving environment. Due to the low probability of exposure to risky events, the testing process is exceedingly time-consuming. To tackle this issue, we proposed a Surrogate-based Monte Carlo Method to accelerate the risk assessment for HAVs in scenario-based simulation. Surrogate Models (SMs) were utilized to approximate the outcomes of untested scenarios, hence facilitating the identification of risky scenarios. Therefore, the large number of samples required by Monte Carlo did not need to be tested entirely. Naturalistic distributions fitted from HighD data was used to generate samples. The Car-following and Cut-in scenarios were selected for the case study, as they represented two distinct testing spaces. As such, the capabilities of different SMs can be further analyzed. We proved that the performances of six mainstream SMs were greatly distinguished from each other. Inverse Distance Weighted (IDW), the proven most capable SM, was combined with Monte Carlo. Compared with Monte Carlo, up to 95% of tested samples can be saved. Compared with the Importance Sampling method, another popular improvement in Monte Carlo simulation, the proposed method can further save 58.1% of CPU execution time.

With the rapid development of autonomous driving, Autonomous Vehicles (AVs) have started to appear on public roads, which has inevitably affected current traffic conditions and the operations of Manual Vehicles (MVs). Current research on AVs’ influence has mainly been conducted at individual level of driving behaviors, while few studies have focused on the overall network level to consider the traffic flow pattern due to mixed traffic. In this work, considering varying signal control schemes and demand loading patterns, we conducted simulation experiments based on a grid network and a real-world network in Beijing using SUMO. Traffic flow with the mixture of MVs, low-level AVs (LAVs), and high-level AVs (HAVs) were emulated so to investigate how the network performs at various levels of mixed traffic. Driving behaviors between the three types of vehicles were calibrated using driving data drawn from OpenACC dataset, and Waymo Open Dataset. The capacity and critical accumulation of the Macroscopic Fundamental Diagram (MFD) were chosen as the key indicators of network performance. We found that AVs positively boost network capacity (up to 19.0% increase) but a negative influence on critical accumulation was also observed (up to 9.0% decrease). However, the positive impact of OpenACC and Waymo’s AVs on macroscopic traffic is still far from ideal since they may be too conservative. AVs can boost flow when traffic is in unsaturated or saturated states. However, when traffic flow is oversaturated, AVs can instead cause flow and average speed to drop faster than that in the MV-only scenario.

One essential goal for Highly Automated Vehicles (HAVs) safety test is to assess their risk rate in naturalistic driving environment, and to compare their performance with human drivers. The probability of exposure to risk events is generally low, making the test process extremely time-consuming. To address this, we proposed a surrogate-based method in scenario-based simulation test to expediate the assessment of the risk rate of HAVs. HighD data were used to fit the naturalistic distribution and to estimate the probability of each concrete scenario. Machine learning model-based surrogates were proposed to quickly approximate the test result of each concrete scenario. Considering the different capabilities and domains of various surrogate models, we applied six surrogate models to search for two types of targeted scenarios with different risk levels and rarity levels. We proved that the performances of different surrogate models greatly distinguish from each other when the target scenarios are extremely rare. Inverse Distance Weighted (IDW) was the most efficient surrogate model, which could achieve risk rate assessment with only 2.5% test resources. The required CPU runtime of IDW was 2% of that required by Kriging. The proposed method has great potential in accelerating the risk assessment of HAVs.

Vehicle trajectories can provide a clear picture of traffic flow, which facilitates traffic state estimation and signal control optimization at intersections. Connected and Automated Vehicles (CAVs) can not only report their own trajectories, but also continuously collect surrounding vehicles’ trajectories using onboard sensors, which creates an opportunity to reconstruct fully-sampled vehicle trajectories. However, this data source brings challenges such as low penetration rate of CAVs and complex detection environment at intersections. To address these problems, this study proposes a novel framework under micro-perspective, in which trajectory estimation and fusion algorithms are integrated. The spatiotemporal correlations of detected trajectories are analyzed and classified into four regions, and four corresponding trajectory estimation algorithms based on extended car-following model are established to estimate the undetected part of each trajectory. Furthermore, a trajectory fusion algorithm based on Particle Filter is developed to fuse the estimated trajectories separately derived from upstream and downstream with minimized errors. The proposed method was comprehensively evaluated at field and simulated signalized intersections. The results show that compared with Variational Theory method, queue location error, time error and cumulative distance error of the proposed method were 76.3%, 44.4% and 54.5% lower, respectively, and the proposed trajectory fusion algorithm improved the accuracy and smoothness with the above three indices decreased by 17.3%, 47.7% and 6.2%, respectively. It was also found that the proposed method can adapt to different traffic conditions and penetration rates of CAVs.

Automated Vehicles (AVs) need to be thoroughly evaluated in order to ensure their driving capabilities. However, comprehensive evaluations are intractable due to both time and monetary costs. To address this problem, we propose an Adaptive Design of Experiments (ADOE) method to evaluate the safety of AVs. Using this method, a Surrogate Model (SM) is established and updated iteratively. SM in ADOE is used to approximate the results of AV testing and help to select the next concrete scenario to be tested in each iteration. Two different ADOE approaches are proposed in this study for different testing purposes. Since the choice of the surrogate model has a profound impact on the performance of the ADOE method, 6 surrogate models were compared with two logical scenarios at different scales – a car following logical scenario and a cut-in logical scenario. Results show that Extreme Gradient Boosting (XGB) is suitable for both ADOE approaches. And both proposed ADOE approaches achieved desired performance. Scenario-oriented ADOE made full use of each concrete scenario, capturing one collision case for every 1.12 test runs in the car following logical scenario, while SM-oriented ADOE successfully depicted the boundary between safety and danger. Using 0.46% test resources compared to enumeration, the SM-oriented ADOE found 93.9% dangerous scenarios with 90.9% precision. ADOE approaches have great potential in accelerating the evaluation of AV safety.

Highly Automated Vehicles (HAVs) must undergo strict safety testing before being released to the public. Mileage-based on-road testing suffers from unaffordable time costs and high safety risks. Simulated scenario-based testing has been found to be a trustworthy alternative for testing HAVs’ built-in algorithms and functionalities. Test automation is typically used to generate target scenarios. This approach facilitates customized testing and avoids wasting time on simple and redundant scenarios. This study aims to review test automation methods and discuss how to accentuate their strengths rather than be trapped in their weaknesses under certain applicable conditions. According to their main purposes, we classify test automation methods into coverage-oriented, unsafe-scenario-oriented, and naturalistic-assessment-oriented categories. To further demonstrate the differences of these methods, we then design numerical experiment to compare the capabilities of seven test automation methods. Finally, we compile our observations to form a comprehensive guide for selecting test automation methods with different test requirements in mind.

Performing a left turn in a non-protected phase at mixed-flow intersections is one of the most challenging driving maneuvers. In general, there are three typical behavioral features during this turning process: multiple conflicting objects, multi-layer interaction between vehicles, non-motorized vehicles, and pedestrians, and long-term interaction event chains. In current studies, few models consider the impact of these three typical features simultaneously. This paper proposes a Conv-LSTM model to predict the positions of turning vehicles at each moment during the turning process in these complicated environments. The model uses convolution, which is an essential part of a convolutional neural network (CNN), to extract higher-level features across different time segments. Afterward, a long short-term memory (LSTM) network is employed to obtain the feature sequence with long-term dependence on the features of historical periods. Finally, the initial prediction of the Conv-LSTM is modified by the non-holonomic constraints of vehicles. Left-turning trajectories extracted from a simulation environment are used for model training and testing. As a result, the Conv-LSTM model performs better compared with the CNN and LSTM models. The average offset of every point on the trajectories is reduced by 50.4% and 37.1%, respectively, relative to the CNN and LSTM, and the interactive behaviors during the left-turn process are well reproduced by the proposed model. Another round of testing shows that the generalization ability of proposed model to real environments was initially proved to be feasible with the extracted ground-truth trajectories in the field environment.