Advanced Air Mobility (AAM) envisions heterogenous airborne entities like crewed and uncrewed passenger and cargo vehicles within, and between urban and rural environment. To achieve this, a paradigm shift to a cooperative operating environment similar to Extensible Traffic Management (xTM) is needed. This requires the blending of Traditional Air Traffic Services (ATS) with the new generation AAM vehicles having their unique flight dynamics and handling characteristics. A hybrid environment needs to be established with enhanced shared situational awareness for all stakeholders, enabling equitable airspace access, minimizing risk, optimized airspace use, and providing flexible and adaptable airspace rules. This paper introduces a novel concept of distributed airspace management which would be apt for all kinds of operational scenarios perceived for AAM. The proposal is centered around the efficiency and safety in air space management being achieved by self-discipline.
There is a growing interest in the concept of a smart city and how these advanced technologies will improve the quality of living and make a city more attractive to visitors, commerce and industry. This course fills an unmet need for defining and explaining the relationship between connected and autonomous vehicles (CAVs) and smart city transportation. It is apparent that CAVs will achieve the best results when integrated with current and emerging urban infrastructure for transportation. This course addresses such integration from technology, organizational, policy and business model perspectives.
Advanced Driver Assist System (ADAS) and autonomous vehicle technologies have disrupted the traditional automotive industry with potential to increase safety and optimize the cost of car ownership. Among the challenges are those of sensing the environment in and around the vehicle. Infrared camera sensing is seeing a rapid growth and adoption in the industry. The applications and illumination architecture options continue to evolve. This course will provide the foundation on which to build near infrared camera technologies for automotive applications.
Autonomous Vehicles are being widely tested under diverse conditions with expectations that they will soon be a regular feature on roads. The development of Autonomous Vehicles has become an important policy in countries around the world, and the technologies developed by countries and car manufacturers are different, and at the same time to adapt to the road environment and traffic management facilities of different countries, so some countries have built self-driving test sites, and the test content is also different, so it is impossible to compare its relative difficulty. This study surveyed experts and scholars to develop a means of weighting the respective difficulty of various autonomous vehicle testing conditions based on the analytic hierarchy process and fuzzy analytic hierarchy process, applied to a sample of 33 sets of testing conditions based on road type, management actions and operational capabilities.
In the process of automobile industrialization, integrated electric drive systems turn to be the mainstream transmission system of electric vehicles gradually. The main sources of noise and vibration in the chassis are from the gear reducer and motor system, as a replacement of engine. For improving the electric vehicles NVH performance, effective identification and quantitative analysis of the main noise sources are a significant basis. Based on the rotating hub test platform in the semi-anechoic chamber, in this experiment, an electric vehicle equipped with a three-in-one electric drive system is taken as the research object. As well the noise and vibration signals in the interior vehicle and the near field of the electric drive system are collected under the operating conditions of uniform speed, acceleration speed, and coasting with gears under different loads, and the test results are processed and analyzed by using the spectral analysis and order analysis theories.
With the advancement of intelligent driving technology, the driving comfort of autonomous vehicles has garnered significant attention. Under highly automated driving conditions, the driver does not need to engage in driving tasks. Since the automation of vehicles will reach a considerable level,the driver inside the vehicle becomes a passenger, and now the study of the passenger experience in automated driving vehicles has become an important research topic. To investigate the effects of automatic driving on passengers' riding experience in vehicle platooning scenarios, the study conducted real vehicle experiments with six participants. The study measured the subjective perception scores, eye movement, and electrocardiogram signals of passengers in the front passenger seat under different vehicle speeds, distances, and driving modes. The results of the statistical analysis show that vehicle speed has the most significant impact on passenger perception.
As a key technology of intelligent transportation system, vehicle type recognition plays an important role in ensuring traffic safety, optimizing traffic management and improving traffic efficiency, which provides strong support for the development of modern society and the intelligent construction of traffic system. Aiming at the problems of large number of parameters, low detection efficiency and poor real-time performance in existing vehicle recognition algorithms, this paper proposes an improved vehicle recognition algorithm based on YOLOv5. Firstly, the lightweight network model MobileNet-V3 is used to replace the backbone feature extraction network CSPDarknet53 of the YOLOv5 model. The parameter quantity and computational complexity of the model are greatly reduced by replacing the standard convolution with the depthwise separable convolution, and enabled the model to maintain higher accuracy while having faster reasoning speed.
Truck platooning is an emerging technology that exploits the drag reduction experienced by bluff bodies moving together in close longitudinal proximity. The drag-reduction phenomenon is produced via two mechanisms: wake-effect drag reduction from leading vehicles, whereby a following vehicle operates in a region of lower apparent wind speed, reducing its drag; and base-drag reduction from following vehicles, whereby the high-pressure field forward of a closely-following vehicle will increase the base pressure of a leading vehicle, reducing its drag. This paper presents an empirical model for calculating the drag-reduction benefits from truck platooning. The model provides a general framework from which the drag reduction of any vehicle in a heterogeneous truck platoon can be calculated, based on its isolated-vehicle drag-coefficient performance and limited geometric considerations.
The advent of Vehicle-to-Everything (V2X) communication has revolutionized the automotive industry, particularly with the rise of Advanced Driver Assistance Systems (ADAS). V2X enables vehicles to communicate not only with each other (V2V) but also with infrastructure (V2I) and pedestrians (V2P), enhancing road safety and efficiency. ADAS, which includes features like adaptive cruise control and automatic intersection navigation, relies on V2X data exchange to make real-time decisions and improve driver assistance capabilities. Over the years, the progress of V2X technology has been marked by standardization efforts, increased deployment, and a growing ecosystem of connected vehicles, paving the way for safer and more efficient autonomous navigation. The EcoCAR Mobility Challenge was a 4-year student competition among 12 universities across the United States and Canada sponsored by the U.S.
Driver steering characteristic clustering aims to deeply understand the driver's behavior and decision-making process through cluster analysis of the driver's turning data. It seeks to gain a better understanding of the diverse steering characteristics exhibited by drivers, providing valuable insights for road safety, driver assistance systems, and traffic management. The primary objective of this study is to delve into the practical application of various clustering algorithms in processing driver steering data, as well as to compare their performance and applicability. In this paper, principal component analysis is employed to reduce the dimensionality of the selected steering feature parameters. Following that, we apply K-means, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise, and other algorithms for clustering analysis. Subsequently, the evaluation of clustering results is conducted using the Calinski-Harabasz Score and Silhouette Coefficient.
Platooning is a coordinated driving strategy by which following trucks are placed into the wake of leading vehicles. Doing this leads to two primary benefits. First, the vehicles following are shielded from aerodynamic drag by a “pulling” effect. Secondly, by placing vehicles behind the leading truck, the leading vehicles experience a “pushing” effect. The reduction in aerodynamic drag leads to reduced fuel usage and, as a consequence, reduced greenhouse gas emissions. In order to maximize these effects, the inter-vehicle distance, or headway, needs to be minimized. In current platooning strategy iterations, Coordinated Adaptive Cruise Control (CACC) is used to maintain close following distances. Many of these strategies utilize the fuel rate signal as a parameter in the controllers cost function. By using fuel rate, current control strategies have limited applicability to non-conventional powertrains.
Mobility is evolving with EVs and e-VTOLs. Conventional vehicle designs were mainly focused on the considerations emerging from the vehicle itself like efficiency, emissions, comfort etc and was independent of changes in other technologies like electrical sector. But, EVs have changed the status quo & this change is prompting the vehicle designers to think broader on the larger EV eco-system including electrical grid. Use of DC battery, popularity of DC fast charging & V2X solutions show the importance of DC power systems. Also, key drivers of electric grid indicate a move towards renewables-based DC microgrids. Thus, overall trends in Electrical & Vehicle sectors shows acceptances of DC systems, making its analysis significant. Compared to AC, an important factor in DC systems is circuit protection which is safety critical.
Collisions resulting in injuries or fatalities occur more frequently at intersections. This is partly because safe navigation of intersections requires drivers to accurately observe and respond to other road users with conflicting paths. Previous studies have raised questions about how traffic control devices and the positioning of other road users might affect drivers' visual search strategies when navigating intersections. To address these questions, four left-turn-across-path (LTAP) scenarios were created by combining two types of traffic control devices (stop signs and traffic lights) with two hazard starting locations (central and peripheral). 74 licensed drivers responded to all scenarios in a counterbalanced order using a full vehicle driving simulator. Eye-tracking glasses were used to monitor eye movements, both before and after hazard onset.