US 11,673,564 B2
Autonomous vehicle safety platform system and method
Jason Ye, Ann Arbor, MI (US); John Cavicchio, Ann Arbor, MI (US); Andres Tamez, Ann Arbor, MI (US); Jacob Lucero, Ann Arbor, MI (US); Justin Tesmer, Ann Arbor, MI (US); Anush Gandra, Ann Arbor, MI (US); Yaxin Luan, Ann Arbor, MI (US); and Shane DeMeulenaere, Ann Arbor, MI (US)
Assigned to May Mobility, Inc., Ann Arbor, MI (US)
Filed by May Mobility, Inc., Ann Arbor, MI (US)
Filed on Jun. 22, 2022, as Appl. No. 17/846,963.
Application 17/846,963 is a continuation of application No. 17/550,461, filed on Dec. 14, 2021, granted, now 11,396,302.
Claims priority of provisional application 63/125,304, filed on Dec. 14, 2020.
Prior Publication US 2022/0324469 A1, Oct. 13, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. B60W 50/029 (2012.01); B60W 60/00 (2020.01); B60W 30/095 (2012.01); B60W 40/04 (2006.01); G06V 20/58 (2022.01); B60W 30/09 (2012.01)
CPC B60W 50/029 (2013.01) [B60W 30/09 (2013.01); B60W 30/0956 (2013.01); B60W 40/04 (2013.01); B60W 60/0011 (2020.02); B60W 60/0015 (2020.02); B60W 60/00274 (2020.02); G06V 20/58 (2022.01); B60W 2050/0292 (2013.01); B60W 2554/40 (2020.02); B60W 2554/80 (2020.02); B60W 2556/50 (2020.02)] 20 Claims
OG exemplary drawing
 
1. An autonomous fallback method for a vehicle, comprising:
receiving an environmental representation which identifies a set of dynamic agents;
based on the environmental representation, determining a set of navigational edge candidates;
classifying a navigational edge candidate of the set of navigational edge candidates as available based on an occupancy prediction corresponding to each dynamic agent of the set of dynamic agents, wherein each dynamic agent is assumed to be static within a planning horizon;
based on the classification of the navigational edge candidate as available, generating a fallback plan based on the navigational edge candidate;
storing the fallback plan at a memory coupled to an embedded controller; and
determining satisfaction of a trigger condition and, in response, autonomously controlling the vehicle based on the fallback plan with the embedded controller.