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

Counterfactual Building and Evaluation via eXplainable Support Vector Data Description

Increasingly in recent times, the mere prediction of a machine learning algorithm is considered insufficient to gain complete control over the event being predicted. A machine learning algorithm should be considered reliable in the way it allows to extract more knowledge and information than just having a prediction at hand. In this perspective, the counterfactual theory plays a central role. By definition, a counterfactual is the smallest variation of the input such that it changes the predicted behaviour. The paper addresses counterfactuals through Support Vector Data Description (SVDD), empowered by explainability and metric for assessing the counterfactual quality. After showing the specific case in which an analytical solution may be found (under Euclidean distance and linear kernel), an optimisation problem is posed for any type of distances and kernels. The vehicle platooning application is the use case considered to demonstrate how the outlined methodology may offer support to safety-critical applications as well as how explanation may shed new light into the control of the system at hand

Pavement Surface Evaluation Using Mobile Terrestrial LiDAR Scanning Systems

Periodic measurement of pavement surfaces for pavement management system (PMS) data collection is vital for state transportation agencies. Vehicle-based mobile light detection and ranging (LiDAR) systems can be used as a versatile tool to collect point data throughout a roadway corridor. The overall goal of this research is to investigate if mobile terrestrial LiDAR Scanning (MTLS) systems can be used as an efficient and effective method to create accurate digital pavement surfaces for. LiDAR data were collected by five MTLS vendors. In particular, the research is interested in three things: 1) how accurate MTLS is for collecting roadway cross slopes; 2) what is the potential for using MTLS digital pavement surfaces to do materials calculations for pavement rehabilitation projects; and 3) examine the benefit of using MTLS to identify pavement rutting locations. Cross slopes were measured at 23 test stations using traditional surveying methods (conventional leveling served as ground-truth) and compared with adjusted and unadjusted MTLS extracted cross slopes. The results indicate that both adjusted and unadjusted MTLS derived cross slopes meet suggested cross slope accuracies (±0.2%). Application of unadjusted MTLS instead of post-processed MTLS point clouds may decrease/eliminate the cost of a control surveys. The study also used a novel approach to process the MTLS data in a geographic information system (GIS) environment to create a 3-dimension raster representation of a roadway surface. MTLS data from each vendor was evaluated in terms of the accuracy and precision of their raster surface. The resultant surfaces were compared between vendors and with a raster surface created from a centerline profile and 100-ft. cross-section data obtained using traditional surveying methods. When comparing LiDAR data between compliant MTLS vendors, average raster cell height differences averaged 0.21 inches, indicating LiDAR data has considerable potential for creating accurate pavement material volume estimates. The application of MTLS data was also evaluated in terms of the accuracy of collected transverse profiles. Transverse profiles captured from MTLS systems have been compared to 2-inch interval field data collection using partial curve mapping (PCM), Frechet distance, area, curve length, and Dynamic Time Warping (DTW) techniques. The results indicated that there is potential for MTLS systems for use in creating an accurate transverse profile for potential identification of pavement rut areas. This research also identified a novel approach for determining pavement rut areas based on the shape of grid cells. This rather simplistic approach is easily implementable on a network wide basis depending on MTLS point cloud availability. The method does not require the calculation/estimation of an ideal surface to determine rut depths/locations

Small business innovation research. Abstracts of completed 1987 phase 1 projects

Non-proprietary summaries of Phase 1 Small Business Innovation Research (SBIR) projects supported by NASA in the 1987 program year are given. Work in the areas of aeronautical propulsion, aerodynamics, acoustics, aircraft systems, materials and structures, teleoperators and robotics, computer sciences, information systems, spacecraft systems, spacecraft power supplies, spacecraft propulsion, bioastronautics, satellite communication, and space processing are covered

Computer Vision-Based Traffic Sign Detection and Extraction: A Hybrid Approach Using GIS And Machine Learning

Traffic sign detection and positioning have drawn considerable attention because of the recent development of autonomous driving and intelligent transportation systems. In order to detect and pinpoint traffic signs accurately, this research proposes two methods. In the first method, geo-tagged Google Street View images and road networks were utilized to locate traffic signs. In the second method, both traffic signs categories and locations were identified and extracted from the location-based GoPro video. TensorFlow is the machine learning framework used to implement these two methods. To that end, 363 stop signs were detected and mapped accurately using the first method (Google Street View image-based approach). Then 32 traffic signs were recognized and pinpointed using the second method (GoPro video-based approach) for better location accuracy, within 10 meters. The average distance from the observation points to the 32 ground truth references was 7.78 meters. The advantages of these methods were discussed. GoPro video-based approach has higher location accuracy, while Google Street View image-based approach is more accessible in most major cities around the world. The proposed traffic sign detection workflow can thus extract and locate traffic signs in other cities. For further consideration and development of this research, IMU (Inertial Measurement Unit) and SLAM (Simultaneous Localization and Mapping) methods could be integrated to incorporate more data and improve location prediction accuracy