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Title: Analytical Longitudinal Speed Planning for CAVs with Previewed Road Geometry and Friction Constraints
Due to the lack of information, current vehicle control systems generally assume that the road friction conditions ahead of a vehicle are unchanged relative to those at the vehicle's current position. This can result in dangerous situations if the friction is suddenly decreasing from the current situation, or overly conservative driving styles if the friction of the current situation is worse than the roadway ahead. However, with connectivity either to other vehicles, infrastructure, or cloud services, future vehicles may have access to upcoming roadway information; this is particularly valuable for planning velocity trajectories that consider the friction and geometry in the road path ahead. This paper introduces a method for planning longitudinal speed profiles for Connected and Autonomous Vehicles (CAVs) that have previewed information about path geometry and friction conditions. The novelty of this approach is to explicitly include consideration of the friction ellipse available along the intended path. The paper derives an analytical solution for certain preview cases that upper-bounds the allowable vehicle velocity profile while preventing departure from the friction ellipse. The results further define the relationship between a lower bound on friction, the path geometry, and minimum friction preview distance. This relationship is used to ensure the vehicle has sufficient time to take action for upcoming hazardous situations. The efficacy of the algorithm is demonstrated through an application case where a vehicle navigates a curving road with changing friction conditions, with results showing that, with sufficient preview, the vehicle could anticipate allowable and stable path keeping speed.  more » « less
Award ID(s):
1932509
NSF-PAR ID:
10395538
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
2021 IEEE International Intelligent Transportation Systems Conference (ITSC)
Page Range / eLocation ID:
1610 to 1615
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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