Motion prediction is essential for autonomous vehicles to navigate complex environments and anticipate the behavior of other traffic participants. As new driving scenarios emerge, models must be continually updated without retraining from scratch. We propose DECODE, a continual learning framework that starts from a pre-trained generalized model and incrementally expands specialized models for distinct domains. Unlike existing approaches that pursue a single unified model, DECODE explicitly balances specialization and generalization through dynamic model selection. It employs a hypernetwork for parameter generation, which reduces storage costs, and utilizes a normalizing flow for real-time domain inference via likelihood estimation. Outputs from specialized and generalized models are fused using Bayesian uncertainty estimation. This integration ensures optimal performance in familiar conditions while maintaining robustness in novel scenarios. Extensive experiments show DECODE achieves a low forgetting rate of 0.044 and an average minADE of 0.584 m, outperforming prior methods and generalizing well across diverse driving domains. Furthermore, we demonstrate that DECODE can be extended beyond motion prediction to general continual learning tasks such as image classification, showcasing its broad applicability.

Infrastructure-based cooperative perception has been researched for several years, but few automotive warning or control applications using this information have been published. Infrastructure sensing, such as with cameras or lidars, and a communication system, allows connected vehicles to receive information about all observed objects. An SAE standard, “V2X Sensor-Sharing for Cooperative and Automated Driving” (J3224), released in 2022, introduces the Sensor Data Sharing Message (SDSM) as the standard communication message for cooperative perception. This paper investigates the use of the SDSM for a vehicle application to provide warnings of potential collisions with vulnerable road users who will cross the street at the intersection. The application was tested in CARLA simulation under various roadside detection errors and communication conditions to assess the impact on the on-board application and estimate the minimum detection and communication requirements for effective use. In addition, the system was implemented and evaluated at the Mcity test facility. The results demonstrate that the proposed warning system can accurately and promptly warn the driver, given specific communication conditions, and show that the SDSM is viable for real-time on-board usage.

Roadside perception has become a critical component for connected and automated vehicles (CAVs), enhancing safety and offering a comprehensive view of the traffic environment that onboard detection systems alone cannot provide. By supplementing the limitations of onboard sensors, roadside perception systems improve the accuracy and reliability of detecting and localizing vehicles and pedestrians in challenging locations. Currently, a variety of cameras, including fisheye and regular cameras, are deployed along roadsides for surveillance purposes. These sensors have significant potential to improve vehicle and pedestrian detection. This paper extends our previous work on single image sensor vehicle detection by developing a comprehensive multiple sensor fusion framework. We take advantage of the complementary strengths of multiple fisheye and regular cameras to enhance the accuracy and robustness of the perception system. The proposed system has been extensively tested in Mcity, a controlled urban testing environment, through numerous field tests. The results demonstrate the effectiveness of our approach, showcasing promising improvements in vehicle and pedestrian detection and tracking accuracy.

This paper presents a traffic oscillation mitigation-oriented optimal control framework for connected and automated vehicles (CAVs) in a mixed traffic environment where the behavior of human-driven vehicles (HVs) is unknown. The primary objective of this framework is to alleviate traffic oscillations, thereby improving overall traffic flow. To achieve this, we introduce a novel total equilibrium spacing estimation method, incorporating stochastic parameters into a car-following model and quantifying the deviation between the mean and equilibrium spacing. This estimation, integrated with a jam-absorption driving strategy, is embedded into a Model Predictive Control (MPC) model for the objective of mitigating traffic oscillations. The efficacy of the proposed control method is evaluated through two experiments utilizing real vehicle trajectory datasets. The first experiment focuses on a single CAV, exploring the impact of key controller parameters on oscillation mitigation. Results demonstrate the optimal performance of the proposed Oscillation Mitigation-based Model Predictive Control (OM-MPC) model, even with a shorter CAV distance (e.g., 100 m), revealing a positive correlation between CAV distance and suitable preset oscillation duration. The second experiment extends the investigation to multiple stop-and-go shockwaves and varying CAV penetration rates. A comparative analysis of control models, including OM-MPC, regular MPC, and proportional-integral with saturation, is conducted based on velocity mean (VM), road segment congestion index (RI), and vehicle stop times (VST). The findings underscore the effectiveness of the proposed control method in mitigating traffic oscillations and enhancing overall traffic efficiency, establishing it as the optimal choice among the three approaches.

Microscopic traffic simulation provides a controllable, repeatable, and efficient testing environment for autonomous vehicles (AVs). To evaluate AVs’ safety performance unbiasedly, the probability distributions of environment statistics in the simulated naturalistic driving environment (NDE) need to be consistent with those from the real-world driving environment. However, although human driving behaviors have been extensively investigated in the transportation engineering field, most existing models were developed for traffic flow analysis without considering the distributional consistency of driving behaviors, which could cause significant evaluation biasedness for AV testing. To fill this research gap, a distributionally consistent NDE modeling framework is proposed in this paper. Using large-scale naturalistic driving data, empirical distributions are obtained to construct the stochastic human driving behavior models under different conditions. To address the error accumulation problem during the simulation, an optimization-based method is further designed to refine the empirical behavior models. Specifically, the vehicle state evolution is modeled as a Markov chain and its stationary distribution is twisted to match the distribution from the real-world driving environment. The framework is evaluated in the case study of a multi-lane highway driving simulation, where the distributional accuracy of the generated NDE is validated and the safety performance of an AV model is effectively evaluated.

Real-time safety metrics are important for automated driving systems (ADS) to assess the risk of driving situations and assist in decision-making. Although a number of real-time safety metrics have been proposed in the literature, there is a lack of systematic performance evaluations of these metrics. As different behavioral assumptions are adopted in different safety metrics, it is difficult to compare the safety metrics and evaluate their performance. To overcome this challenge, in this study, we propose an evaluation framework utilizing logged vehicle trajectory data so that vehicle trajectories for both the subject vehicle (SV) and background vehicles (BVs) are obtained and the prediction errors caused by behavioral assumptions can be eliminated. Specifically, we examine whether the SV is in a collision unavoidable situation at each moment, given all near-future trajectories of BVs. In this way, we level the ground for a fair comparison of different safety metrics, as a good safety metric should always alarm in advance to the collision unavoidable moment. When trajectory data from a large number of trips are available, we can systematically evaluate and compare different metrics’ statistical performance. In the case study, three representative real-time safety metrics, including the time-to-collision (TTC), the PEGASUS Criticality Metric (PCM) and the Model Predictive Instantaneous Safety Metric (MPrISM), are evaluated using a large-scale simulated trajectory dataset. The results demonstrate that the MPrISM achieves the highest recall and the PCM has the best accuracy. The proposed evaluation framework is important for researchers, practitioners, and regulators to characterize different metrics, and to select appropriate metrics for different applications. Moreover, by conducting failure analysis on moments when a safety metric fails, we can identify its potential weaknesses, which can be valuable for potential refinements and improvements.

Safety performance evaluation is critical for developing and deploying connected and automated vehicles (CAVs). One prevailing way is to design testing scenarios using prior knowledge of CAVs, test CAVs in these scenarios, and then evaluate their safety performances. However, significant differences between CAVs and prior knowledge could severely reduce the evaluation efficiency. Towards addressing this issue, most existing studies focus on the adaptive design of testing scenarios during the CAV testing process, but so far they cannot be applied to high-dimensional scenarios. In this paper, we focus on the adaptive safety performance evaluation by leveraging the testing results, after the CAV testing process. It can significantly improve the evaluation efficiency and be applied to high-dimensional scenarios. Specifically, instead of directly evaluating the unknown quantity (e.g., crash rates) of CAV safety performances, we evaluate the differences between the unknown quantity and known quantity (i.e., control variates). By leveraging the testing results, the control variates could be well-designed and optimized such that the differences are close to zero, so the evaluation variance could be dramatically reduced for different CAVs. To handle the high-dimensional scenarios, we propose the sparse control variates method, where the control variates are designed only for the sparse and critical variables of scenarios. According to the number of critical variables in each scenario, the control variates are stratified into strata and optimized within each stratum using multiple linear regression techniques. We justify the proposed method’s effectiveness by rigorous theoretical analysis and empirical study of high-dimensional overtaking scenarios.

GPS spoofing attacks pose great challenges to connected vehicle (CVs) safety applications and localization of autonomous vehicles (AVs). In this paper, we propose to utilize transportation and vehicle engineering domain knowledge to detect GPS spoofing attacks towards CVs and AVs. A novel detection method using learning from demonstration is developed, which can be implemented in both vehicles and at the transportation infrastructure. A computational-efficient driving model, which can be learned from historical trajectories of the vehicles, is constructed to predict normal driving behaviors. Then a statistical method is developed to measure the dissimilarities between the observed trajectory and the predicted normal trajectory for anomaly detection. We validate the proposed method using two threat models (i.e., attacks targeting the multi-sensor fusion system of AVs and attacks targeting the intersection movement assist application of CVs) on two real-world datasets (i.e., KAIST and Michigan roundabout dataset). Results show that the proposed model is able to detect almost all of the attacks in time with low false positive and false negative rates.

Vehicle behavior prediction in complex urban scenarios with traffic signals and interactive agents is an important yet complicated task for autonomous vehicles (AVs). In this work, a hierarchical vehicle behavior prediction framework is proposed to incorporate the traffic signal information and model the interaction between vehicles. The framework predicts vehicle behaviors in two stages, discrete intention prediction and continuous trajectory prediction. In the discrete intention prediction stage, Bayesian network is adopted to provide a high-level behavior prediction of the principle other vehicle. The discrete prediction results are forwarded to the second stage, where a continuous trajectory is predicted with maximum entropy inverse reinforcement learning and potential game. The framework is designed to be able to capture the difference among human drivers with parameterized driver characteristics. The proposed predictor is validated in two scenarios: the yellow light running scenario and the right-turn scenario. The trajectory prediction average displacement error of the yellow light running scenario is 0.695m for a 3-second prediction interval, and the prediction accuracy of the right-turn vehicle in the right-turn scenario is 0.51m for a 2-second prediction interval.

We propose a novel and pragmatic framework for traffic scene perception with roadside cameras. The proposed framework covers a full-stack of roadside perception pipeline for infrastructure-assisted autonomous driving, including object detection, object localization, object tracking, and multi-camera information fusion. Unlike previous vision-based perception frameworks rely upon depth offset or 3D annotation at training, we adopt a modular decoupling design and introduce a landmark-based 3D localization method, where the detection and localization can be well decoupled so that the model can be easily trained based on only 2D annotations. The proposed framework applies to either optical or thermal cameras with pinhole or fish-eye lenses. Our framework is deployed at a two-lane roundabout located at Ellsworth Rd. and State St., Ann Arbor, MI, USA, providing$7\times 24$real-time traffic flow monitoring and high-precision vehicle trajectory extraction. The whole system runs efficiently on a low-power edge computing device with all-component end-to-end delay of less than 20ms.

Queue length estimation plays an important role in traffic signal control and performance measures of signalized intersections. Traditionally, queue lengths are estimated by applying the shockwave theory to loop detector data. In recent years, the tremendous amount of vehicle trajectory data collected from probe vehicles such as ride-hailing vehicles and connected vehicles provides an alternative approach to queue length estimation. To estimate queue lengths cycle by cycle, many existing methods require the knowledge of the probe vehicle penetration rate and queue length distribution. However, the estimation of the two parameters has not been well studied. This paper proposes a maximum likelihood estimation method that can estimate the parameters from historical probe vehicle data. The maximum likelihood estimation problem is solved by the expectation-maximization (EM) algorithm iteratively. Validation results show that the proposed method could estimate the parameters accurately and thus enable the existing methods to estimate queue lengths cycle by cycle.

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this article, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate for the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a newly designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study is investigated and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

Connected vehicle (CV) technology brings both opportunities and challenges to the traffic signal control (TSC) system. While safety and mobility performance could be greatly improved by adopting CV technologies, the connectivity between vehicles and transportation infrastructure may increase the risks of cyber threats. In the past few years, studies related to cybersecurity on the TSC systems were conducted. However, there still lacks a systematic investigation that provides a comprehensive analysis framework. In this study, our aim is to fill the research gap by proposing a comprehensive analysis framework for the cybersecurity problem of the TSC in the CV environment. With potential threats towards the major components of the system and their corresponding impacts on safety and efficiency analyzed, data spoofing attack is considered the most plausible and realistic attack approach. Based on this finding, different attack strategies and defense solutions are discussed. A case study is presented to show the impact of the data spoofing attacks towards a selected CV based TSC system and corresponding mitigation countermeasures. This case study is conducted on a hybrid security testing platform, with virtual traffic and a real V2X communication network. To the best of our knowledge, this is the first study to present a comprehensive analysis framework to the cybersecurity problem of the CV-based TSC systems.

Testing scenario library generation (TSLG) is a critical step for the development and deployment of connected and automated vehicles (CAVs). In Part I of this study, a general method for TSLG is proposed, and theoretical properties are investigated regarding the accuracy and efficiency of CAV evaluation. This paper aims to provide implementation examples and guidelines, and to enhance the proposed methodology under high-dimensional scenarios. Three typical cases, including cut-in, highway-exit, and car-following, are designed and studied in this paper. For each case, the process of library generation and CAV evaluation is elaborated. To address the challenges brought by high dimensionality, the proposed method is further enhanced by reinforcement learning technique. For all three cases, results show that the proposed method can accelerate the CAV evaluation process by multiple magnitudes with same evaluation accuracy, if compared with the on-road test method.

Testing and evaluation is a critical step in the development and deployment of connected and automated vehicles (CAVs), and yet there is no systematic framework to generate testing scenario library. This study aims to provide a general framework for the testing scenario library generation (TSLG) problem with different operational design domains (ODDs), CAV models, and performance metrics. Given an ODD, the testing scenario library is defined as a critical set of scenarios that can be used for CAV test. Each testing scenario is evaluated by a newly proposed measure, scenario criticality, which can be computed as a combination of maneuver challenge and exposure frequency. To search for critical scenarios, an auxiliary objective function is designed, and a multi-start optimization method along with seed-filling is applied. Theoretical analysis suggests that the proposed framework can obtain accurate evaluation results with much fewer number of tests, if compared with the on-road test method. In part II of the study, three case studies are investigated to demonstrate the proposed method. Reinforcement learning based technique is applied to enhance the searching method under high-dimensional scenarios.

Queue length is a crucial measure of intersection performance. Probe vehicles (PVs) with advanced sensors are capable of recording vehicle trajectories that can be used to estimate queue length, a technique of which has received considerable attention in the past decade. Noticeably, this technique usually requires high PV penetration rates (e.g., above 25%) in order to ensure estimation accuracy. Though the PVs are expected to increase, their penetration rate will still remain relatively low in the near future. Meanwhile, the initial queue length is another important factor that directly relates to queue dynamics at each cycle. However, most of the studies failed to adequately account for the effect of the initial queue on cyclic queue length estimation. To address the above challenges, this paper proposes a cycle-based end of queue estimation method using sampled vehicle trajectory data under relatively low penetration rates. Two major steps are involved: first, vehicle arrival process is modeled as a certain distribution in line with traffic conditions and an expectation maximum (EM) procedure is employed to estimate the arrival rate of each cycle; then, both ends of the queue and initial queue are estimated at each cycle based on shockwave theory. Microscopic traffic simulator VISSIM is utilized to examine the performance of the method. The experimental results reveal that the cycle-based end of the queue can be estimated with desirable accuracy in different scenarios, e.g., undersaturated, oversaturated, and queue spillback conditions. The comparison with the state-of-the-art methods further helps to verify the advantage of the method, especially under low-penetration-rate conditions.

Origin-Destination (OD) flow data is an important instrument for traffic study and management. So far traditional ways like surveys or detectors are costly and only give limited availability of OD flows. Various statistical and stochastic models for OD flow estimation and prediction based on limited link volume data or automatic vehicle identification (AVI) data have been developed. However, smartphone-generated trajectory data has not been as much leveraged in this field, though the usage of smartphones in traveling is emerging in recent years. In this paper, we propose a semi-supervised deep learning based model that appropriately combines both AVI and smartphone trajectory data during training and is able to generate predictions of OD flows in an urban network solely based on the smartphone trajectory data at inference time. Our model can provide OD estimation and prediction services on larger spatial areas beyond the limited spatial coverage of AVI data. Tests of our model using real data have shown promising results, compared with an AVI input-dependent Kalman filter model. Potentially, our model can easily be embedded to a trajectory collecting platform and generate continuous real-time OD flow predictions online.

Similitude theory, particularly dimension analysis, is a common tool for testing scaled-down engineering models and is widely used in vehicle dynamics and other engineering fields. However, it is barely employed in scaling traffic flow dynamics. In this paper, dimension analysis is adopted to scale car-following dynamics. Under the guidance of the similitude theory, a scaled downvehicle test bed is built where seven cars are running on a circular track at a maximum speed initially and congestion emerges after a period of time. In other words, a phantom traffic jam appears in our similitude test bed without any bottlenecks. The fundamental diagrams drawn from the experimental results show that our test bed has the capability of generating traffic hysteresis that is commonly observed in the field. Therefore, the design of this test bed can be used to simulate traffic dynamics to a certain degree and will pave the way for scaled-down connected and automated vehicle systems development.

Testing and evaluation are critical steps in the development of connected and automated vehicle (CAV) technology. One limitation of closed CAV testing facilities is that they merely provide empty roadways, in which testing CAVs can only interact with a limited number of other CAVs and infrastructure. This paper presents an augmented reality environment for CAV testing and evaluation. A real-world testing facility and a simulation platform are combined together. Movements of testing CAVs in the real world are synchronized with simulation and information of background traffic is fed back to testing CAVs. Testing CAVs can interact with virtual background traffic as if in a realistic traffic environment. The proposed system mainly consists of three components: a simulation platform, testing CAVs, and a communication network. Testing scenarios that have safety concerns and/or require interactions with other vehicles can be performed. Two exemplary test scenarios are designed and implemented to demonstrate the capabilities of the system.

In this paper, we present an adaptive signal control scheme to prevent intersection traffic blockage resulted from vehicle queue spillover. A method to identify vehicle queue spillover condition through simplified shockwave analysis is developed. Instead of measuring the vehicle queue length or locating the end of queue directly, this method relies on the vehicle speed which is more feasible to measure in practice. The adaptive traffic signal control scheme is designed to prevent potential intersection traffic blockage, and adaptively allocates green time to appropriate signal phases. At the end, a simulation study is carried out to evaluate the proposed adaptive control scheme. The results show that the scheme can effectively prevent intersection traffic blockage and significantly improve the performance of the intersection in terms of vehicle delay.