This project will develop social behaviors for autonomous freeway driving. Human drivers signal their intentions to one another in various ways, including adjustments in speed and acceleration. The project team will refine previously developed intention prediction models and couple them with host-vehicle strategy generation methods to improve the performance and realism of autonomous vehicles interacting with other freeway traffic in a wide range of scenarios, with a primary emphasis on entrance/exit ramp handling and secondary emphasis on lane changing. The developed algorithms will be tested on Carnegie Mellon University's (CMU’s) autonomous Cadillac SRX and, if possible, on relevant vehicles in General Motor's (GM’s) continuing vehicle autonomy projects. Relevance to safety is twofold: use of such algorithms (1) in driver assistance systems to convey to drivers the intentions of surrounding cars; and (2) in autonomous cars to provide less “robotic” and more natural, predictable, and socially comfortable driving.
Prediction and Behaviors for Driver Assistance and Socially Cooperative Autonomous Driving
2018
20 pages
Report
Keine Angabe
Englisch
Transportation Safety , Transportation & Traffic Planning , Regional Administration & Planning , Urban Administration & Planning , Transportation , Road Transportation , Common Carrier & Satellite , Communications , Driver support systems , Data and Information Technology , Safety and Human Factors , Algorithms , Behavior , Drivers , Freeways , Intelligent vehicles , Social factors , Vehicle and equipment , Carnegie Mellon University
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