Aiming at the problem of driver's perception lag and low utilization efficiency of space-time resources in expressway ramp confluence area, based on the preemptive spatiotemporal trajectory Adjustment system, from the perspective of coordinating spatiotemporal resources, the reasonable value of safe space-time distance in trajectory pre-preparation is quantitatively analyzed. The minimum safety gap required for ramp vehicles to merge into the mainline is analyzed by introducing double positioning error and spatiotemporal trajectory tracking error. A merging control strategy for autonomous driving heterogeneous vehicles is proposed, which integrates vehicle type, driving intention, and safety spatiotemporal distance. The specific confluence strategies of ramp target vehicles and mainline cooperative vehicles under different vehicle types are systematically expounded. A variety of traffic flow and speed scenarios are used for full combination simulation. By comparing the time-position-speed diagram, the vehicle operation characteristics and the dynamic difference of confluence are qualitatively analyzed, and the average speed and average delay are used as the evaluation indices to quantitatively evaluate the performance advantages of the preemptive cooperative confluence control strategy. The results show that the maximum average delay improvement rates of mainline and ramp vehicles are 90.24 % and 74.24 %, respectively. The proposed strategy can effectively avoid potential vehicle conflicts and emergency braking behaviors, improve driving safety in the confluence area, and show significant advantages in driving stability and overall traffic efficiency optimization.

This significant contribution makes it a critical sector for climate change mitigation, as reducing emissions from transportation is essential for achieving global climate goals. The sector's transformation through electrification, automation, and intelligent infrastructure offers promising avenues for substantial emissions reductions (Sciarretta et al., 2020; International Energy Agency, 2023; McKinsey Center for Future Mobility, 2023). However, the success of these innovations is critically dependent on the availability of suitable and accurate emission estimation models to guide the design and deployment of new technologies. Motor Vehicle Emission Simulation (MOVES) (U.S. Environmental Protection Agency, 2022), one of the most well-established emission estimation models, serves as the official and state-of-the-art emission estimation model in the U.S., provided, enforced, and maintained by the U.S. Environmental Protection Agency (EPA). Despite its technical certification, MOVES' processing and software is tailored for two specific governmental uses: State Implementation Plans and Conformity Analyses U.S. Environmental Protection Agency (2021), which are for states to achieve and maintain air quality standards; and its use beyond trained practitioners and these specific analyses poses two main limitations. First, a steep learning curve, computational demands, and complex inputs make it difficult for researchers and practitioners to use. In particular, MOVES has rigid input requirements, including a combination of toggle-based settings within its GUI and structured input files in specific formats. Second, MOVES is tailored for macroscopic analysis and is unsuitable for microscopic applications, such as control and optimization, which commonly require second-by-second emission calculations for individual actions and vehicles.

Vehicle detection is a critical component in the development of advanced intelligent transportation systems (ITS), which rely on accurate and real-time information to optimize traffic flow, enhance safety, and support autonomous vehicle technologies [1]. As the number of vehicles on the road continues to grow, the demand for robust vehicle detection systems capable of operating under varying conditions--such as changes in weather, lighting, and vehicle types--has become paramount. In traffic monitoring, vehicle detection enables the real-time analysis of traffic patterns, congestion management, and incident detection, contributing to more efficient urban mobility. Moreover, vehicle detection serves as the foundation for vehicle classification and tracking systems, which are essential for dynamic tolling, traffic law enforcement, and infrastructure planning [2]. The evolution of vehicle detection systems has been closely tied to advancements in deep learning, particularly in the field of convolutional neural networks (CNNs) [3]. CNNs have played a pivotal role in object detection tasks due to their ability to automatically learn hierarchical features from raw image data [4, 5, 6, 7, 8]. Traditional vehicle detection approaches, such as histogram of oriented gradients (HOG)[9] and support vector machines (SVM)[10], lacked the flexibility and scalability needed for modern applications, especially when dealing with complex scenes and varying environmental conditions. Subsequent methods, like Scale-Invariant Feature Transform (SIFT) and Speeded-Up Robust Features (SURF) [11], introduced improvements in detecting and describing features under varying scale and rotation conditions, although computational constraints limited real-time applicability for ITS.

Urban intersections, filled with a diverse mix of vehicles from small cars to large semi-trailers, present a persistent challenge for traffic control and management. This reality drives our investigation into how robot vehicles (RVs) can transform such heterogeneous traffic flow, particularly at unsignalized intersections where traditional control methods often falter during power failures and emergencies. Using reinforcement learning (RL) and real-world traffic data, we study heterogeneous mixed traffic across complex intersections under gradual automation by varying RV penetration from 10% to 90%. The results are compelling: average waiting times decrease by up to 86% and 91% compared to signalized and unsignalized intersections, respectively. Additionally, we uncover a "rarity advantage," where less frequent vehicles, such as trucks, benefit the most from RV coordination (by up to 87%). RVs' presence also leads to lower CO2 emissions and fuel consumption compared to managing traffic via traffic lights. Moreover, space headways decrease across all vehicle types as RV rate increases, indicating better road space utilization.

With fully automated driving systems (ADS; SAE level 4) ride-hailing services expanding in the US, we are now approaching an inflection point, where the process of retrospectively evaluating ADS safety impact can start to yield statistically credible conclusions. An ADS safety impact measurement requires a comparison to a "benchmark" crash rate. This study aims to address, update, and extend the existing literature by leveraging police-reported crashes to generate human crash rates for multiple geographic areas with current ADS deployments. All of the data leveraged is publicly accessible, and the benchmark determination methodology is intended to be repeatable and transparent. Generating a benchmark that is comparable to ADS crash data is associated with certain challenges, including data selection, handling underreporting and reporting thresholds, identifying the population of drivers and vehicles to compare against, choosing an appropriate severity level to assess, and matching crash and mileage exposure data. Consequently, we identify essential steps when generating benchmarks, and present our analyses amongst a backdrop of existing ADS benchmark literature. One analysis presented is the usage of established underreporting correction methodology to publicly available human driver police-reported data to improve comparability to publicly available ADS crash data. We also identify important dependencies in controlling for geographic region, road type, and vehicle type, and show how failing to control for these features can bias results. This body of work aims to contribute to the ability of the community - researchers, regulators, industry, and experts - to reach consensus on how to estimate accurate benchmarks.

The transportation sector accounts for about 25% of global greenhouse gas emissions. Therefore, an improvement of energy efficiency in the traffic sector is crucial to reducing the carbon footprint. Efficiency is typically measured in terms of energy use per traveled distance, e.g. liters of fuel per kilometer. Leading factors that impact the energy efficiency are the type of vehicle, environment, driver behavior, and weather conditions. These varying factors introduce uncertainty in estimating the vehicles' energy efficiency. We propose in this paper an ensemble learning approach based on deep neural networks (ENN) that is designed to reduce the predictive uncertainty and to output measures of such uncertainty. We evaluated it using the publicly available Vehicle Energy Dataset (VED) and compared it with several baselines per vehicle and energy type. The results showed a high predictive performance and they allowed to output a measure of predictive uncertainty.

Accurate vehicle type classification serves a significant role in the intelligent transportation system. It is critical for ruler to understand the road conditions and usually contributive for the traffic light control system to response correspondingly to alleviate traffic congestion. New technologies and comprehensive data sources, such as aerial photos and remote sensing data, provide richer and high-dimensional information. Also, due to the rapid development of deep neural network technology, image based vehicle classification methods can better extract underlying objective features when processing data. Recently, several deep learning models have been proposed to solve the problem. However, traditional pure convolutional based approaches have constraints on global information extraction, and the complex environment, such as bad weather, seriously limits the recognition capability. To improve the vehicle type classification capability under complex environment, this study proposes a novel Densely Connected Convolutional Transformer in Transformer Neural Network (Dense-TNT) framework for the vehicle type classification by stacking Densely Connected Convolutional Network (DenseNet) and Transformer in Transformer (TNT) layers. Three-region vehicle data and four different weather conditions are deployed for recognition capability evaluation. Experimental findings validate the recognition ability of our proposed vehicle classification model with little decay, even under the heavy foggy weather condition.

We develop a generic mechanism for generating vehicle-type specific sequences of waypoints from a probabilistic foundation model of driving behavior. Many foundation behavior models are trained on data that does not include vehicle information, which limits their utility in downstream applications such as planning. Our novel methodology conditionally specializes such a behavior predictive model to a vehicle-type by utilizing byproducts of the reinforcement learning algorithms used to produce vehicle specific controllers. We show how to compose a vehicle specific value function estimate with a generic probabilistic behavior model to generate vehicle-type specific waypoint sequences that are more likely to be physically plausible then their vehicle-agnostic counterparts.

What if I told you that a network that classifies 10 different types of vehicles can provide useful knowledge for a classification problem with 3 different types of cars? This is called transfer learning – a method that uses pre-trained neural networks to solve a new, similar problem. Over the years, people have been trying to produce different methods to train neural networks with small amounts of data. Those methods are used to generate more data for training. However, transfer learning provides an alternative by learning from existing architectures (trained on large datasets) and further training them for our new problem.

Tesla has slowly added more visualizations to the car display, showing what the car can detect and respond to in its environment. Tesla initially showed just road markings and some vehicles, but then slowly added more vehicle types, pedestrians and traffic cones. However, with the release of FSD Beta version 9, Tesla has drastically increased the amount of objects the car can visualize and interact with. The visualizations in the car aren't tied one-to-one with what the car is capable of detecting and using to make decisions. However, Tesla keeps visualizations and object detection closely coupled so that drivers have a good understanding of what the car can see.