Transfusor: Transformer Diffusor for Controllable Human-like Generation of Vehicle Lane Changing Trajectories

With ongoing development of autonomous driving systems and increasing desire for deployment, researchers continue to seek reliable approaches for ADS systems. The virtual simulation test (VST) has become a prominent approach for testing autonomous driving systems (ADS) and advanced driver assistance systems (ADAS) due to its advantages of fast execution, low cost, and high repeatability. However, the success of these simulation-based experiments heavily relies on the realism of the testing scenarios. It is needed to create more flexible and high-fidelity testing scenarios in VST in order to increase the safety and reliabilityof ADS and ADAS.To address this challenge, this paper introduces the "Transfusor" model, which leverages the transformer and diffusor models (two cutting-edge deep learning generative technologies). The primary objective of the Transfusor model is to generate highly realistic and controllable human-like lane-changing trajectories in highway scenarios. Extensive experiments were carried out, and the results demonstrate that the proposed model effectively learns the spatiotemporal characteristics of humans' lane-changing behaviors and successfully generates trajectories that closely mimic real-world human driving. As such, the proposed model can play a critical role of creating more flexible and high-fidelity testing scenarios in the VST, ultimately leading to safer and more reliable ADS and ADAS.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar

PFL-LSTR: A privacy-preserving framework for driver intention inference based on in-vehicle and out-vehicle information

Intelligent vehicle anticipation of the movement intentions of other drivers can reduce collisions. Typically, when a human driver of another vehicle (referred to as the target vehicle) engages in specific behaviors such as checking the rearview mirror prior to lane change, a valuable clue is therein provided on the intentions of the target vehicle's driver. Furthermore, the target driver's intentions can be influenced and shaped by their driving environment. For example, if the target vehicle is too close to a leading vehicle, it may renege the lane change decision. On the other hand, a following vehicle in the target lane is too close to the target vehicle could lead to its reversal of the decision to change lanes. Knowledge of such intentions of all vehicles in a traffic stream can help enhance traffic safety. Unfortunately, such information is often captured in the form of images/videos. Utilization of personally identifiable data to train a general model could violate user privacy. Federated Learning (FL) is a promising tool to resolve this conundrum. FL efficiently trains models without exposing the underlying data. This paper introduces a Personalized Federated Learning (PFL) model embedded a long short-term transformer (LSTR) framework. The framework predicts drivers' intentions by leveraging in-vehicle videos (of driver movement, gestures, and expressions) and out-of-vehicle videos (of the vehicle's surroundings - frontal/rear areas). The proposed PFL-LSTR framework is trained and tested through real-world driving data collected from human drivers at Interstate 65 in Indiana. The results suggest that the PFL-LSTR exhibits high adaptability and high precision, and that out-of-vehicle information (particularly, the driver's rear-mirror viewing actions) is important because it helps reduce false positives and thereby enhances the precision of driver intention inference.Comment: Submitted for presentation only at the 2024 Annual Meeting of the Transportation Research Boar

Tradeoffs between safe/comfortable headways versus mobility-enhancing headways in an automated driving environment: preliminary insights using a driving simulator experiment

Purpose – The anticipated benefits of connected and autonomous vehicles (CAVs) include safety and mobility enhancement. Small headways between successive vehicles, on one hand, can cause increased capacity and throughput and thereby improve overall mobility. On the other hand, small headways can cause vehicle occupant discomfort and unsafety. Therefore, in a CAV environment, it is important to determine appropriate headways that offer a good balance between mobility and user safety/comfort. Design/methodology/approach – In addressing this research question, this study carried out a pilot experiment using a driving simulator equipped with a Level-3 automated driving system, to measure the threshold headways. The Method of Constant Stimuli (MCS) procedure was modified to enable the estimation of two comfort thresholds. The participants (drivers) were placed in three categories (“Cautious,” “Neutral” and “Confident”) and 250 driving tests were carried out for each category. Probit analysis was then used to estimate the threshold headways that differentiate drivers' discomfort and their intention to re-engage the driving tasks. Findings – The results indicate that “Cautious” drivers tend to be more sensitive to the decrease in headways, and therefore exhibit greater propensity to deactivate the automated driving mode under a longer headway relative to other driver groups. Also, there seems to exist no driver discomfort when the CAV maintains headway up to 5%–9% shorter than the headways they typically adopt. Further reduction in headways tends to cause discomfort to drivers and trigger take over control maneuver. Research limitations/implications – In future studies, the number of observations could be increased further. Practical implications – The study findings can help guide specification of user-friendly headways specified in the algorithms used for CAV control, by vehicle manufacturers and technology companies. By measuring and learning from a human driver's perception, AV manufacturers can produce personalized AVs to suit the user's preferences regarding headway. Also, the identified headway thresholds could be applied by practitioners and researchers to update highway lane capacities and passenger-car-equivalents in the autonomous mobility era. Originality/value – The study represents a pioneering effort and preliminary pilot driving simulator experiment to assess the tradeoffs between comfortable headways versus mobility-enhancing headways in an automated driving environment