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Formation and reconfiguration of tight multi-lane platoons

Journal Article · · Control Engineering Practice
 [1];  [2];  [2]
  1. Univ. of California, Berkeley, CA (United States). Dept. of Mechanical Engineering; Univ. of California, Berkeley, CA (United States)
  2. Univ. of California, Berkeley, CA (United States). Dept. of Mechanical Engineering

Advances in vehicular communication technologies are expected to facilitate cooperative driving in the future. Connected and Automated Vehicles (CAVs) are able to collaboratively plan and execute driving maneuvers by sharing their perceptual knowledge and future plans. In this paper, an architecture for autonomous navigation of tight multi-lane platoons traveling on public roads is presented. Using the proposed approach, CAVs are able to form single or multi-lane platoons of various geometrical configurations. They are able to reshape and adjust their configurations according to changes in the environment. The proposed architecture consists of two main components: an offline motion planner system and an online hierarchical control system. The motion planner uses an optimization-based approach for cooperative formation and reconfiguration in tight spaces. A constrained optimization scheme is used to plan smooth, dynamically feasible and collision-free trajectories for all the vehicles within the platoon. The paper addresses online computation limitations by employing a family of maneuvers precomputed offline and stored on a look-up table on the vehicles. The online hierarchical control system is composed of three levels: a traffic operation system (TOS), a decision-maker, and a path-follower. The TOS determines the desired platoon reconfiguration. The decision-maker checks the feasibility of the reconfiguration plan based on real-time information about the surrounding traffic. The reconfiguration maneuver is executed by a low-level path-following feedback controller in real-time. The effectiveness of the approach is demonstrated through simulations of three case studies: (1) formation reconfiguration (2) obstacle avoidance, and (3) benchmarking against behavior-based planning in which the desired formation is achieved using a sequence of motion primitives.

Research Organization:
Univ. of California, Oakland, CA (United States)
Sponsoring Organization:
USDOE Advanced Research Projects Agency - Energy (ARPA-E)
Grant/Contract Number:
AR0000791
OSTI ID:
1848256
Alternate ID(s):
OSTI ID: 1778544
Journal Information:
Control Engineering Practice, Journal Name: Control Engineering Practice Journal Issue: C Vol. 108; ISSN 0967-0661
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English

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