RAG-based Explainable Prediction of Road Users Behaviors for Automated Driving using Knowledge Graphs and Large Language Models Two use cases have been implemented following the proposed approach: 1) Prediction of pedestrians' crossing actions; 2) Prediction of lane change maneuvers... MM Hussien,AN ...
Planning-based Prediction for Pedestrians, Ziebart B. et al. (2009). 🎞️ Learning for autonomous navigation, Bagnell A. et al. (2010). Learning Autonomous Driving Styles and Maneuvers from Expert Demonstration, Silver D. et al. (2012). Learning Driving Styles for Autonomous Vehicles from ...
Planning-based prediction for pedestrians In this paper, we describe a novel uncertaintybased technique for predicting the future motions of a moving person. Our model assumes that people behave pu... BD Ziebart,ND Ratliff,G Gallagher,... - IEEE 被引量: 370发表: 2009年 A METHODOLOGY FOR AUT...
Traffic Safety Facts—Pedestrians. NHTSA, 2012. http://www.nhtsa.gov/Pedestrians. Accessed July 31, 2013. Google Scholar2. Torbic, D. J., Harwood, D. W., Bokenkroger, C. D., Srinivasan, R., Carter, D., Zegeer, C. V., and Lyon, C. Pedestrian Safety Prediction Methodology for ...
http://www.nhtsa.gov/Pedestrians. Accessed July 31, 2013. Google Scholar2. Torbic, D. J., Harwood, D. W., Bokenkroger, C. D., Srinivasan, R., Carter, D., Zegeer, C. V., and Lyon, C. Pedestrian Safety Prediction Methodology for Urban Signalized Intersections. In Transportation ...
(i.e., building height - H, distance between buildings or width of the street - W) are firstly optimized by guaranteeing either access to or shading from sunlight, accordingly to the specific needs (e.g., direct access to sunlight is preferable for PV modules, not always for pedestrians ...
We further introduce a Rapidly-Exploring Random Tree (RRT)-based path planner developed for planning in state-time space. With the help of an improved distance metric, the planner is much faster than RRT-Blossom [11] in complex maps. In an environment with 10 pedestrians, the planner and ...
where Nc is the control time domain, and Np is the prediction time domain. 4.2 Cost Function Considering Anti-Rollover APF MPC is based on the design of a cost function and its constraints. The cost function should be capable of ensuring the control quantity required for the vehicle's rapid...
and pedestrians will all have different behaviors and motion models. Constraining our motion plan based on the behavior of these other agents will often involve prediction, which is subject to uncertainty. However, if we take the conservative approach and constrain ourselves to all possible behaviors...
It is very necessary for an intelligent heavy truck to have the ability to prevent rollover independently. However, it was rarely considered in intelligent vehicle motion planning. To improve rollover stability, a motion planning strategy with autonomous