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

Moment-based space-variant Shack-Hartmann wavefront reconstruction

Based on image moment theory, an approach for space-variant Shack-Hartmann wavefront reconstruction is presented in this article. The relation between the moment of a pair of subimages and the local transformation coefficients is derived. The square guide 'star' is used to obtain a special solution from this relation. The moment-based wavefront reconstruction has a reduced computational complexity compared to the iteration-based algorithm. Image restorations are executed by the tiling strategy with 5 $\times$ 5 PSFs as well as the conventional strategy with a global average PSF. Visual and quantitative evaluations support our approach.Comment: This paper has been accepted for publication in the journal Optics Communications on April 12th, 202

Heavy Fleet and Facilities Optimization

The Indiana Department of Transportation (INDOT) is responsible for timely clearance of snow on state-maintained highways in Indiana as part of its wintertime operations. For this and other maintenance purposes, the state’s subdistricts maintain 101 administrative units spread throughout the state. These units are staffed by personnel, including snow truck drivers and house snow removal trucks and other equipment. INDOT indicated a need to carry out value engineering analysis of the replacement timing of the truck fleet. To address these questions, this study carried out analysis to ascertain the appropriate truck replacement age, that is different across each of the state\u27s three weather-based regions to minimize the truck life cycle cost. INDOT also indicated a need for research guidance in possible revisions to the administrative unit locations and optimal routes to be taken by trucks in each unit in order to reduce deadhead miles. For purposes of optimizing the truck snow routes, the study developed two alternative algorithmic approaches. The first uses mathematical programming to select work packets for trucks while ensuring that snow is cleared at all snow routes and allowing the users to identify optimal route and unit location. The second approach uses network routing concepts, such as the rural postman problem, and allows the user to change the analysis inputs, such as the number of available drivers and so on. The first approach developed using opensolver (an open source tool with excel) and the second approach coded as an electronic tool, are submitted as part of this report. Both approaches can be used by INDOT’s administrative unit managers for decision support regarding the deployment of resources for snow clearing operations and to minimize the associated costs

Collision Avoidance Framework for Autonomous Vehicles Under Crash Imminent Situations

Ninety-five percent of all roadway crashes are attributed fully or partially to human error, and a multitude of safety-related programs, policies, and initiatives have seen limited success in reducing roadway crashes and their accompanying fatalities, injuries, and property damage. For this reason, safety professionals have lauded the emergence of autonomous vehicles (AVs) as a promising palliative to the persistent problem of road crashes. Such optimism is reflected in recent literature that have argues from a conceptual standpoint, that road safety enhancement will be one of the prospective benefits of AV operations because automation removes humans from vehicle driving operations and therefore criminates or mitigates human error. It can be argued that the safety benefits of AVs will be manifest when AV market penetration reaches 100%. However, it seems clear from a practical standpoint that the transition from a system of exclusively humandriven vehicles (HDVs) to that of exclusively AVs will not only be necessary but also an arduous journey. This transition period will be characterized by heterogeneous traffic, where human-driven vehicles (HDVs) and AVs share the road space, and whence the prospective safety benefits of AVs may not be fully realized due to human error arising from the HDV operations in the mixed traffic space. These traffic conflicts, which may lead to collisions, could arise from any of several contexts of driving maneuvers, one of which is aggressive lane changes, the focus of this thesis. From the literature, it is clear that lane changing is inherently more collision-prone compared to most other maneuvers including car following, and therefore the consequences of errant human driving behavior such as inattention of misjudgment during lane changing, are more severe. To address this problem, this thesis developed a control framework to be used by AVs to help them avoid collision in a mixed traffic stream with human drivers who exhibit aggressive lane-changing behavior. The developed framework, which is based on a Model Predictive Control (MPC) approach, is designed to control the AV’s movements safely by duly accommodating potential human error from the HDVs that could otherwise lead to any of two common collision patterns: rear-end and side-impact. Further, the thesis investigated how connectivity between the HDVs, and AVs could facilitate joint operational decision-making and sharing of real-time information, thereby further enhancing the safety of the entire traffic stream. Finally, the thesis presents the results of driving simulations carried out to test and validate the performance of the control framework under different traffic conditions

<b>Safety and mobility improvement of mixed traffic using optimization- And Learning-based methods</b>

Traffic safety and congestion are global concerns. Autonomous vehicles (AVs) are expected to enhance transportation safety and reduce congestion. However, achieving their full potential requires 100% market penetration, a challenging task. This study addresses key issues in mixed traffic environments, where human-driven vehicles (HDVs) and connected autonomous vehicles (CAVs) coexist. A number of critical questions persist: 1) inadequate exploration of human errors (errors originating from non-CAV sources) in mixed traffic; 2): limited focus on information selection and learning efficiency in network-level rerouting, particularly in highly dynamic environments; 3) inadequacy of personalized element driver inputs in motion-planning frameworks; 4) lack of consideration of user privacy concerns.With the goal of advancing the existing knowledge in this field and shedding light on these matters, this dissertation introduces multiple frameworks. These frameworks leverage connectivity and automation to improve safety and mobility in mixed traffic, addressing various research levels, including local-level and network-level safety enhancement, as well as network-level and global-level mobility enhancement. With optimization- and learning-based methods implemented (Model Predictive Control, Deep Neural Network, Deep Reinforcement Learning, Transformer model and Federated Learning), frameworks introduced in this dissertation are expected to help highway agencies and vehicle manufacturers improve the safety and efficiency of traffic flow in the mixed-traffic era. Our research findings revealed increased crash-avoidance rates in critical situations, enhanced accuracy in predicting lane changes, improved dynamic rerouting within urban areas, and the implementation of effective data-sharing mechanisms with a focus on user privacy. This research underscores the potential of connectivity and automation to significantly enhance mixed-traffic safety and mobility.</p

A Framework for Lane-Change Maneuvers of Connected Autonomous Vehicles in a Mixed-Traffic Environment

In the transition era towards connected autonomous vehicles (CAVs), the sharing of the roadway by CAVs and human-driven vehicles (HDVs) in a mixed-traffic stream is expected to pose safety and flow efficiency concerns even though CAVs may tend to adopt rather conservative maneuvering policies. Unfortunately, this will likely cause HDV drivers to unduly exploit such conservativeness by driving in ways that imperil safety. A context of this situation is lane-changing by the CAV, a potential major source of traffic disturbance at multi-lane highways that could impair their traffic flow efficiency. In dense, high-speed traffic conditions, it will be extremely unsafe for the CAV to change lanes without cooperation from neighboring vehicles in the traffic stream. To help address this issue, this paper developed a framework through which connected HDVs (CHDVs) could cooperate to facilitate safe and efficient lane-changing by the CAV. A numerical experiment was carried out to demonstrate the efficacy of the framework. The results indicated the CAVs’ lane-changing feasibility and the overall duration of the lane-changing if the CAV carries out that maneuver. It was observed that throughout the lane-changing process, the safety of not only the CAV but also of all neighboring vehicles, was promoted through the framework’s collision avoidance mechanism. The overall traffic flow efficiency was analyzed in terms of the ambient level of CHDV–CAV cooperation. Overall, the results of the study present evidence of how CHDV–CAV cooperation can help enhance the overall system efficiency

Scalable Traffic Signal Controls Using Fog-Cloud Based Multiagent Reinforcement Learning

Optimizing traffic signal control (TSC) at intersections continues to pose a challenging problem, particularly for large-scale traffic networks. It has been shown in past research that it is feasible to optimize the operations of individual TSC systems or a small collection of such systems. However, it has been computationally difficult to scale these solution approaches to large networks partly due to the curse of dimensionality that is encountered as the number of intersections increases. Fortunately, recent studies have recognized the potential of exploiting advancements in deep and reinforcement learning to address this problem, and some preliminary successes have been achieved in this regard. However, facilitating such intelligent solution approaches may require large amounts of infrastructure investments such as roadside units (RSUs) and drones, to ensure that connectivity is available across all intersections in the large network. This represents an investment that may be burdensome for the road agency. As such, this study builds on recent work to present a scalable TSC model that may reduce the number of enabling infrastructure that is required. This is achieved using graph attention networks (GATs) to serve as the neural network for deep reinforcement learning. GAT helps to maintain the graph topology of the traffic network while disregarding any irrelevant information. A case study is carried out to demonstrate the effectiveness of the proposed model, and the results show much promise. The overall research outcome suggests that by decomposing large networks using fog nodes, the proposed fog-based graphic RL (FG-RL) model can be easily applied to scale into larger traffic networks