Jonathan Sprinkle

Raphael E. Stern, Shumo Cui, Maria Laura Delle Monache et al.

Traffic waves are phenomena that emerge when the vehicular density exceeds a critical threshold. Considering the presence of increasingly automated vehicles in the traffic stream, a number of research activities have focused on the influence of automated vehicles on the bulk traffic flow. In the present article, we demonstrate experimentally that intelligent control of an autonomous vehicle is able to dampen stop-and-go waves that can arise even in the absence of geometric or lane changing triggers. Precisely, our experiments on a circular track with more than 20 vehicles show that traffic waves emerge consistently, and that they can be dampened by controlling the velocity of a single vehicle in the flow. We compare metrics for velocity, braking events, and fuel economy across experiments. These experimental findings suggest a paradigm shift in traffic management: flow control will be possible via a few mobile actuators (less than 5%) long before a majority of vehicles have autonomous capabilities.

Derek Gloudemans, Gergely Zachár, Yanbing Wang et al.

This work introduces a multi-camera tracking dataset consisting of 234 hours of video data recorded concurrently from 234 overlapping HD cameras covering a 4.2 mile stretch of 8-10 lane interstate highway near Nashville, TN. The video is recorded during a period of high traffic density with 500+ objects typically visible within the scene and typical object longevities of 3-15 minutes. GPS trajectories from 270 vehicle passes through the scene are manually corrected in the video data to provide a set of ground-truth trajectories for recall-oriented tracking metrics, and object detections are provided for each camera in the scene (159 million total before cross-camera fusion). Initial benchmarking of tracking-by-detection algorithms is performed against the GPS trajectories, and a best HOTA of only 9.5% is obtained (best recall 75.9% at IOU 0.1, 47.9 average IDs per ground truth object), indicating the benchmarked trackers do not perform sufficiently well at the long temporal and spatial durations required for traffic scene understanding.

Evelyn Zhang, Alex Richardson, Jonathan Sprinkle

The goal of this paper is to explore the accuracy of dashcam footage to predict the actual kinematic motion of a car-like vehicle. Our approach uses ground truth information from the vehicle's on-board data stream, through the controller area network, and a time-synchronized dashboard camera, mounted to a consumer-grade vehicle, for 18 hours of footage and driving. The contributions of the paper include neural network models that allow us to quantify the accuracy of predicting the vehicle speed and yaw, as well as the presence of a lead vehicle, and its relative distance and speed. In addition, the paper describes how other researchers can gather their own data to perform similar experiments, using open-source tools and off-the-shelf technology.

Zhiyao Zhang, Gergely Zachár, William Barbour et al.

Advanced Traffic Signal Control (TSC) algorithms require real-time phase control, yet existing Hardware-in-the-Loop Simulation (HILS) testbeds only support pre-programmed timing plans. In this paper, we present the first HILS testbed for real-time phase control. We develop a novel middleware architecture that translates dynamic phase actions (selection, switch, and duration) into commands for NTCIP-compliant commercial hardware controllers. This middleware manages phase transitions, synchronizes signal states, and handles errors without interrupting the hardware's internal operations. Experimental validation demonstrates that the system executes real-time phase commands, handles system conflicts, and achieves a low system internal latency at sub-millisecond on average.

Junyi Ji, Derek Gloudemans, Gergely Zachár et al.

Real-world potential of stop-and-go wave smoothing at scale remains largely unquantified. Smoothing freeway traffic waves requires creating a gap so the wave can dissipate, but the gap suggested is often too large and impractical. We propose a counterfactual wave smoothing benchmark that reconstructs a smooth and feasible trajectory from each empirical trajectory by solving a quadratic program with fixed boundary conditions and a maximum allowable gap constraint. We estimate the emission reduction potential from trajectories using the MOVES model. Applying the framework to nine weeks of weekday peak traffic data, featuring rich day-to-day stop-and-go wave dynamics, from the I-24 MOTION testbed, we find meaningful reduction potential under a 0.1-mile maximum gap: average CO2 reductions of 7.92% to 12.04% across lanes, with concurrent reductions of 14.30% to 28.91% CO, 23.15% to 29.42% HC, and 24.37% to 30.98% NOx. Our analysis also quantifies the trade-off between maximum allowable gap opening and emissions benefits.

Xia Wang, Anda Liang, Jonathan Sprinkle et al.

Many decision-making scenarios in modern life benefit from the decision support of artificial intelligence algorithms, which focus on a data-driven philosophy and automated programs or systems. However, crucial decision issues related to security, fairness, and privacy should consider more human knowledge and principles to supervise such AI algorithms to reach more proper solutions and to benefit society more effectively. In this work, we extract knowledge-based logic that defines risky driving formats learned from public transportation accident datasets, which haven't been analyzed in detail to the best of our knowledge. More importantly, this knowledge is critical for recognizing traffic hazards and could supervise and improve AI models in safety-critical systems. Then we use automated verification methods to verify the robustness of such logic. More specifically, we gather 72 accident datasets from Data.gov and organize them by state. Further, we train Decision Tree and XGBoost models on each state's dataset, deriving accident judgment logic. Finally, we deploy robustness verification on these tree-based models under multiple parameter combinations.

Ziyan An, Xia Wang, Hendrik Baier et al.

In response to the lack of trust in Artificial Intelligence (AI) for sequential planning, we design a Computational Tree Logic-guided large language model (LLM)-based natural language explanation framework designed for the Monte Carlo Tree Search (MCTS) algorithm. MCTS is often considered challenging to interpret due to the complexity of its search trees, but our framework is flexible enough to handle a wide range of free-form post-hoc queries and knowledge-based inquiries centered around MCTS and the Markov Decision Process (MDP) of the application domain. By transforming user queries into logic and variable statements, our framework ensures that the evidence obtained from the search tree remains factually consistent with the underlying environmental dynamics and any constraints in the actual stochastic control process. We evaluate the framework rigorously through quantitative assessments, where it demonstrates strong performance in terms of accuracy and factual consistency.

Fang-Chieh Chou, Marsalis Gibson, Rahul Bhadani et al.

Motivated by earlier work and the developer of a new algorithm, the FollowerStopper, this article uses reachability analysis to verify the safety of the FollowerStopper algorithm, which is a controller designed for dampening stop- and-go traffic waves. With more than 1100 miles of driving data collected by our physical platform, we validate our analysis results by comparing it to human driving behaviors. The FollowerStopper controller has been demonstrated to dampen stop-and-go traffic waves at low speed, but previous analysis on its relative safety has been limited to upper and lower bounds of acceleration. To expand upon previous analysis, reachability analysis is used to investigate the safety at the speeds it was originally tested and also at higher speeds. Two formulations of safety analysis with different criteria are shown: distance-based and time headway-based. The FollowerStopper is considered safe with distance-based criterion. However, simulation results demonstrate that the FollowerStopper is not representative of human drivers - it follows too closely behind vehicles, specifically at a distance human would deem as unsafe. On the other hand, under the time headway-based safety analysis, the FollowerStopper is not considered safe anymore. A modified FollowerStopper is proposed to satisfy time-based safety criterion. Simulation results of the proposed FollowerStopper shows that its response represents human driver behavior better.

George Gunter, Huichen Li, Avesta Hojjati et al.

We demonstrate that a supply-chain level compromise of the adaptive cruise control (ACC) capability on equipped vehicles can be used to significantly degrade system level performance of current day mixed-autonomy freeway networks. Via a simple threat model which causes random deceleration attacks (RDAs), compromised vehicles create congestion waves in the traffic that decrease average speed and network throughput. We use a detailed and realistic traffic simulation environment to quantify the impacts of the attack on a model of a real high-volume freeway in the United States. We find that the effect of the attack depends both on the level of underlying traffic congestion, and what percentage of ACC vehicles can be compromised. In moderate congestion regimes the attack can degrade mean commuter speed by over 7%. In high density regimes overall network throughput can be reduced by up to 3%. And, in moderate to high congestion regimes, it can cost commuters on the network over 300 USD/km hr. All of these results motivate that the proposed attack is able to significantly degrade performance of the traffic network. We also develop an anomaly detection technique that uses GPS traces on vehicles to identify malicious/compromised vehicles. We employ this technique on data from the simulation experiments and find that it is unable to identify compromised ACCs compared to benign/normal drivers. That is, these attacks are stealthy to detection. Stronger attacks can be accurately labeled as malicious, motivating that there is a limit to how impactful attacks can be before they are no longer stealthy. Finally, we experimentally execute the attack on a real and commercially available ACC vehicle, demonstrating the possible real world feasibility of an RDA.

George Gunter, Derek Gloudemans, Raphael E. Stern et al.

In this article, we assess the string stability of seven 2018 model year adaptive cruise control (ACC) equipped vehicles that are widely available in the US market. Seven distinct vehicle models from two different vehicle makes are analyzed using data collected from more than 1,200 miles of driving in car-following experiments with ACC engaged by the follower vehicle. The resulting dataset is used to identify the parameters of a linear second order delay differential equation model that approximates the behavior of the black box ACC systems. The string stability of the data-fitted model associated with each vehicle is assessed, and the main finding is that all seven vehicle models have string unstable ACC systems. For one commonly available vehicle model that offers ACC as a standard feature on all trim levels, we validate the string stability finding with a multi-vehicle platoon experiment in which all vehicles are the same year, make, and model. In this test, an initial disturbance of 6 mph is amplified to a 25 mph disturbance, at which point the last vehicle in the platoon is observed to disengage the ACC. The data collected in the driving experiments is made available, representing the largest publicly available comparative driving dataset on ACC equipped vehicles.

Rahul Kumar Bhadani, Jonathan Sprinkle, Matthew Bunting

This paper presents the CAT Vehicle (Cognitive and Autonomous Test Vehicle) Testbed: a research testbed comprised of a distributed simulation-based autonomous vehicle, with straightforward transition to hardware in the loop testing and execution, to support research in autonomous driving technology. The evolution of autonomous driving technology from active safety features and advanced driving assistance systems to full sensor-guided autonomous driving requires testing of every possible scenario. However, researchers who want to demonstrate new results on a physical platform face difficult challenges, if they do not have access to a robotic platform in their own labs. Thus, there is a need for a research testbed where simulation-based results can be rapidly validated through hardware in the loop simulation, in order to test the software on board the physical platform. The CAT Vehicle Testbed offers such a testbed that can mimic dynamics of a real vehicle in simulation and then seamlessly transition to reproduction of use cases with hardware. The simulator utilizes the Robot Operating System (ROS) with a physics-based vehicle model, including simulated sensors and actuators with configurable parameters. The testbed allows multi-vehicle simulation to support vehicle to vehicle interaction. Our testbed also facilitates logging and capturing of the data in the real time that can be played back to examine particular scenarios or use cases, and for regression testing. As part of the demonstration of feasibility, we present a brief description of the CAT Vehicle Challenge, in which student researchers from all over the globe were able to reproduce their simulation results with fewer than 2 days of interfacing with the physical platform.

Tihamer Levendovszky, Bernhard Rumpe, Bernhard Schätz et al.

As complex software and systems development projects need models as an important planning, structuring and development technique, models now face issues resolved for software earlier: models need to be versioned, differences captured, syntactic and semantic correctness checked as early as possible, documented, presented in easily accessible forms, etc. Quality management needs to be established for models as well as their relationship to other models, to code and to requirement documents precisely clarified and tracked. Business and product requirements, product technologies as well as development tools evolve. This also means we need evolutionary technologies both for models within a language and if the language evolves also for an upgrade of the models. This chapter discusses the state of the art in model management and evolution and sketches what is still necessary for models to become as usable and used as software.

Jonathan Sprinkle, Bernhard Rumpe, Hans Vangheluwe et al.

This chapter discusses the current state of the art, and emerging research challenges, for metamodelling. In the state-of-the-art review on metamodelling, we review approaches, abstractions, and tools for metamodelling, evaluate them with respect to their expressivity, investigate what role(s) metamodels may play at run-time and how semantics can be assigned to metamodels and the domain specific modeling languages they could define. In the emerging challenges section on metamodelling we highlight research issues regarding the management of complexity, consistency, and evolution of metamodels, and how the semantics of metamodels impacts each of these.