Transport Research International Documentation (TRID) https://trid.trb.org/ en-us Copyright © 2024. National Academy of Sciences. All rights reserved. http://blogs.law.harvard.edu/tech/rss tris-trb@nas.edu (Bill McLeod) tris-trb@nas.edu (Bill McLeod) Transport Research International Documentation (TRID) https://trid.trb.org/Images/PageHeader-wTitle.jpg https://trid.trb.org/ Two-Sided Deep Reinforcement Learning for Dynamic Mobility-on-Demand Management with Mixed Autonomy https://trid.trb.org/View/2264450 Tue, 19 Dec 2023 09:14:48 GMT https://trid.trb.org/View/2264450 Equilibrium Traffic Dynamics with Mixed Autonomous and Human-Driven Vehicles and Novel Traffic Management Policies: The Effects of Value-of-Time Compensation and Random Road Capacity https://trid.trb.org/View/2265641 Fri, 01 Dec 2023 16:47:30 GMT https://trid.trb.org/View/2265641 Competency-based assessment of pilots’ manual flight performance during instrument flight training https://trid.trb.org/View/2274324 Fri, 17 Nov 2023 11:25:02 GMT https://trid.trb.org/View/2274324 Investigating the Effects of Alcohol Consumption on Manual and Automated Driving https://trid.trb.org/View/2166471 Thu, 04 May 2023 11:51:01 GMT https://trid.trb.org/View/2166471 Cognitive Architecture Based Mental Workload Evaluation for Spatial Fine Manual Control Task https://trid.trb.org/View/1972670 Fri, 21 Apr 2023 09:51:59 GMT https://trid.trb.org/View/1972670 A Less-Disturbed Ecological Driving Strategy for Connected and Automated Vehicles https://trid.trb.org/View/2108174 Mon, 13 Mar 2023 10:23:39 GMT https://trid.trb.org/View/2108174 Shifts in perspective: Operational aspects in (non-)autonomous ride-pooling simulations https://trid.trb.org/View/2038899 Wed, 16 Nov 2022 11:36:31 GMT https://trid.trb.org/View/2038899 An eco-driving algorithm based on vehicle to infrastructure (V2I) communications for signalized intersections https://trid.trb.org/View/2024567 Wed, 19 Oct 2022 09:26:41 GMT https://trid.trb.org/View/2024567 Reasoning Graph: A Situation-aware framework for cooperating unprotected turns under mixed connected and autonomous traffic environments https://trid.trb.org/View/2002447 Wed, 21 Sep 2022 09:21:15 GMT https://trid.trb.org/View/2002447 Road Boundary-Enhanced Automatic Background Filtering for Roadside Lidar Sensors https://trid.trb.org/View/1997804 road-boundary-enhanced, 3D-density statistic filtering (3D-DSFRB). This algorithm involves the boundary of the historical trajectories of road users as the region of interest (ROI) to enhance the accuracy of background filtering. A revised grid-based method was developed for road-boundary ID. The 3D-DSF was only applied for the area outside of the ROI. Within the ROI, only ground surface was excluded. Case studies were conducted to evaluate the effectiveness of the 3D-DSFRB algorithm. The results showed that the 3D-DSFRB can filter background points for both free-flow conditions and congested traffic conditions. The time cost of the 3D-DSFRB was also reduced compared to the 3D-DSF. Compared to the state of the art, the 3D-DSFRB improved the accuracy of background filtering.]]> Wed, 14 Sep 2022 09:11:21 GMT https://trid.trb.org/View/1997804 Dynamic Coordinated Speed Control and Synergistic Performance Evaluation in Connected and Automated Vehicle Environment https://trid.trb.org/View/2010010 Fri, 19 Aug 2022 15:10:55 GMT https://trid.trb.org/View/2010010 Time Required for Take-over from Automated to Manual Driving https://trid.trb.org/View/1834120 Wed, 23 Feb 2022 16:16:10 GMT https://trid.trb.org/View/1834120 A Safety Concept based on a Safety Sustainer for Highly Automated Driving Systems https://trid.trb.org/View/1834098 Wed, 23 Feb 2022 16:16:10 GMT https://trid.trb.org/View/1834098 Operational analysis of an innovative semi-autonomous on-demand transportation system https://trid.trb.org/View/1892337 Mon, 06 Dec 2021 08:43:43 GMT https://trid.trb.org/View/1892337 A risk field-based metric correlates with driver’s perceived risk in manual and automated driving: A test-track study https://trid.trb.org/View/1892322 steering=0.69, ρspeed=0.64), as well as correlated with the driver’s perceived risk in curve driving (r² = 0.98) and while negotiating a car parked outside the lane boundary (r²=0.59). In conclusion, the DRF-based risk estimate (rˆ) is predictive of manual driving behaviour and perceived risk in automated driving. Future research should include tactical and strategic components to the driving task.]]> Mon, 06 Dec 2021 08:43:43 GMT https://trid.trb.org/View/1892322