Improving Passing Lane Safety and Efficiency for Alaska’s Rural Non‐divided Highways

INE/AUTC 14.0

MotionLM: Multi-Agent Motion Forecasting as Language Modeling

Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences of discrete motion tokens and cast multi-agent motion prediction as a language modeling task over this domain. Our model, MotionLM, provides several advantages: First, it does not require anchors or explicit latent variable optimization to learn multimodal distributions. Instead, we leverage a single standard language modeling objective, maximizing the average log probability over sequence tokens. Second, our approach bypasses post-hoc interaction heuristics where individual agent trajectory generation is conducted prior to interactive scoring. Instead, MotionLM produces joint distributions over interactive agent futures in a single autoregressive decoding process. In addition, the model's sequential factorization enables temporally causal conditional rollouts. The proposed approach establishes new state-of-the-art performance for multi-agent motion prediction on the Waymo Open Motion Dataset, ranking 1st on the interactive challenge leaderboard.Comment: To appear at the International Conference on Computer Vision (ICCV) 202

Advanced Toll Information System and Toll Lane Configuration to Reduce Collision Risk

This study assessed the impacts of presence and location of the toll information system on the traffic performance and safety at toll plaza on the Gordie Howe International Bridge. The toll information displays the information on toll payment methods (manual toll collection (MTC), automatic toll collection (ATC) and electronic toll collection (ETC)) for cars or heavy vehicles (HV) via variable message signs (VMS) upstream of toll booth. The study also assessed the impacts of the toll information system with different toll lane configuration for current traffic demand and different percentages of heavy vehicles (HV) to reduce the collision risk at toll plaza. To evaluate the impacts, three scenarios (no VMS, VMS 140 m from the entry gate, and separate VMS for car and HV 75 m before the merge point) were developed and compared using the VISSM microscopic traffic simulation model. Results show that VMS before the merge point had marginal benefit of reducing average delay and reduced rear-end and lane-change collision risk compared to the no VMS scenario. Results also show that converting the toll lanes with multiple toll payment methods to ETC-only lanes with the VMS before the merge point reduced the delay and rear-end and lane-change collision risk compared to the current configuration. Moreover, increasing the number of HV-only lanes from 3 to 4 for higher percentage of HVs with the VMS before the merge point marginally reduced the delay but increased lane-change collision risk compared to the current configuration. This indicates that the installation of ETC-only lanes can potentially improve traffic performance and safety for the current traffic demand but increasing the number of HV-only lanes for higher percentage of HVs can degrade the safety benefit of the system. This study demonstrates that toll lane configuration must be controlled to accommodate varying traffic demand to enhance the effectiveness the toll information system in improving traffic performance and safety

Validation of New Technology using Legacy Metrics: Examination of Surf-IA Alerting for Runway Incursion Incidents

New flight deck technology designed to mitigate runway incursions may not be effective in triggering a flight deck alert to avoid high speed surface collisions for runway incursions classified as serious by legacy metrics. This study demonstrated an innovative method of utilizing expert raters and actual high-risk incidents to identify shortcomings of using legacy metrics to measure the effectiveness of new technology designed to mitigate hazardous incidents. Expert raters were used to validate the Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF-IA) model for providing alerts to pilots to reduce the occurrence of pilot deviation type runway incursion incidents categorized as serious (Category A or B) by the FAA/ICAO Runway Incursion Severity Classification (RISC) model. This study used archival data from Aviation Safety Information Analysis and Sharing (ASIAS) incident reports and video reenactments developed by the FAA Office of Runway Safety. Two expert raters reviewed nine pilot deviation type serious runway incursion incidents. The raters applied the baseline minimally compliant implementation of the RTCA/DO 323 SURF-IA model to determine which incidents would have an alerting SURF-IA outcome. Inter-rater reliability was determined by percentage agreement and Cohen’s kappa and indicated perfect agreement between the raters who assessed six of the incidents with a SURF-IA alerting outcome and three as non-alerting. Specific aircraft states were identified in the baseline SURF-IA model that precluded an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model: (a) wrong runway departures, (b) no alert if traffic entered runway after ownship lift-off from same runway, and (c) helicopter operations. The study concluded that the SURF-IA model did not yield an outcome of a Warning or Caution alert for all pilot deviation type runway incursion incidents classified as serious by the FAA/ICAO RISC model. Even if the SURF-IA model had performed to design, the best it could have achieved would have been a 70% alerting outcome for incidents classified as serious by the legacy RISC model metric. In the qualitative analysis both raters indicated that neither the legacy RISC definition of on-runway nor the SURF-IA definition was appropriate. Hence, the raters’ recommendation was not to adopt either model’s definition, but rather develop an entirely new definition through further study. The raters were explicit about the criticality of appropriate and harmonized definitions used in the models. The different outcomes between the RISC and SURF-IA models may result in misleading information when using the reduction in serious runway incursion incidents as a metric for the benefit of SURF-IA technology. It is recommended that prior to using the ASIAS runway incursion data as a metric for the benefit of SURF-IA, the FAA develop a process for identifying and tracking ASIAS reported PD type serious runway incursion incidents which will not trigger a SURF-IA alert. Consideration should be made to improving the SURF-IA model technical capabilities to accommodate all possible aircraft states that the RISC model would classify as serious runway incursion incidents

Impacts of Connected and Automated Vehicles on Energy and Traffic Flow: Optimal Control Design and Verification Through Field Testing

This dissertation assesses eco-driving effectiveness in several key traffic scenarios that include passenger vehicle transportation in highway driving and urban driving that also includes interactions with traffic signals, as well as heavy-duty line-haul truck transportation in highway driving with significant road grade. These studies are accomplished through both traffic microsimulation that propagates individual vehicle interactions to synthesize large-scale traffic patterns that emerge from the eco-driving strategies, and through experimentation in which real prototyped connected and automated vehicles (CAVs) are utilized to directly measure energy benefits from the designed eco-driving control strategies. In particular, vehicle-in-the-loop is leveraged for the CAVs driven on a physical test track to interact with surrounding traffic that is virtually realized through said microsimulation software in real time. In doing so, model predictive control is designed and implemented to create performative eco-driving policies and to select vehicle lane, as well as enforce safety constraints while autonomously driving a real vehicle. Ultimately, eco-driving policies are both simulated and experimentally vetted in a variety of typical driving scenarios to show up to a 50% boost in fuel economy when switching to CAV drivers without compromising traffic flow. The first part of this dissertation specifically assesses energy efficiency of connected and automated passenger vehicles that exploit intention-sharing sourced from both neighboring vehicles in a highway scene and from traffic lights in an urban scene. Linear model predictive control is implemented for CAV motion planning, whereby chance constraints are introduced to balance between traffic compactness and safety, and integer decision variables are introduced for lane selection and collision avoidance in multi-lane environments. Validation results are shown from both large-scale microsimulation and through experimentation of real prototyped CAVs. The second part of this dissertation then assesses energy efficiency of automated line-haul trucks when tasked to aerodynamically platoon. Nonlinear model predictive control is implemented for motion planning, and simulation and experimentation are conducted for platooning verification under highway conditions with traffic. Then, interaction-aware and intention-sharing cooperative control is further introduced to eliminate experimentally measured platoon disengagements that occur on real highways when using only status-sharing control. Finally, the performance of automated drivers versus human drivers are compared in a point-to-point scenario to verify fundamental eco-driving impacts -- experimentally showing eco-driving to boost energy economy by 11% on average even in simple driving scenarios

Aeronautical Engineering: A special bibliography with indexes, supplement 62

This bibliography lists 306 reports, articles, and other documents introduced into the NASA scientific and technical information system in September 1975

Potential impacts of advanced aerodynamic technology on air transportation system productivity

Summaries of a workshop held at NASA Langley Research Center in 1993 to explore the application of advanced aerodynamics to airport productivity improvement are discussed. Sessions included discussions of terminal area productivity problems and advanced aerodynamic technologies for enhanced high lift and reduced noise, emissions, and wake vortex hazard with emphasis upon advanced aircraft configurations and multidisciplinary solution options

Feature Papers of Drones - Volume I

[EN] The present book is divided into two volumes (Volume I: articles 1–23, and Volume II: articles 24–54) which compile the articles and communications submitted to the Topical Collection ”Feature Papers of Drones” during the years 2020 to 2022 describing novel or new cutting-edge designs, developments, and/or applications of unmanned vehicles (drones). Articles 1–8 are devoted to the developments of drone design, where new concepts and modeling strategies as well as effective designs that improve drone stability and autonomy are introduced. Articles 9–16 focus on the communication aspects of drones as effective strategies for smooth deployment and efficient functioning are required. Therefore, several developments that aim to optimize performance and security are presented. In this regard, one of the most directly related topics is drone swarms, not only in terms of communication but also human-swarm interaction and their applications for science missions, surveillance, and disaster rescue operations. To conclude with the volume I related to drone improvements, articles 17–23 discusses the advancements associated with autonomous navigation, obstacle avoidance, and enhanced flight plannin

Aeronautical Engineering: A special bibliography with indexes, supplement 55

This bibliography lists 260 reports, articles, and other documents introduced into the NASA scientific and technical information system in February 1975