US 12,472,981 B2
Systems and methods for decision making for autonomous vehicles
Stefano Di Cairano, Newton, MA (US); Abraham Vinod, Cambridge, MA (US); Terrence Skibik, Boulder, CO (US); Avishai Weiss, Boston, MA (US); and Karl Berntorp, Newton, MA (US)
Assigned to Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed by Mitsubishi Electric Research Laboratories, Inc., Cambridge, MA (US)
Filed on Aug. 16, 2023, as Appl. No. 18/234,408.
Claims priority of provisional application 63/524,529, filed on Jun. 30, 2023.
Prior Publication US 2025/0002042 A1, Jan. 2, 2025
Int. Cl. B60W 60/00 (2020.01); B60W 30/095 (2012.01); B60W 30/18 (2012.01)
CPC B60W 60/0011 (2020.02) [B60W 30/0956 (2013.01); B60W 30/18163 (2013.01); B60W 2554/4041 (2020.02)] 20 Claims
OG exemplary drawing
 
1. A control system configured for controlling a motion of an ego-vehicle traveling to a target destination, comprising:
a memory configured to store multiple trajectory-generating functions, wherein each of the multiple trajectory-generating functions corresponds to a maneuver defined by a parameter vector associated with a driving decision of a plurality of driving decisions, wherein the parameter vector is defined by one or multiple parameters, wherein each of the multiple trajectory-generating functions is configured to generate a sequence of states within a prediction horizon, from a given value of the parameter vector and a current state of the ego-vehicle, wherein at least two of the multiple trajectory-generating functions are configured for different types of parameters of the parameter vector; and
at least one processor coupled with stored instructions, which when executed by the processor, cause the control system to test control admissibility of at least some of the driving decisions consistent with the target destination of the ego-vehicle at the current state, wherein, for testing, the processor is configured to:
map a tested driving decision to a target region of states of the ego-vehicle at an instance of time within the prediction horizon;
collect one or multiple obstacle states of one or multiple obstacles proximate to the ego-vehicle at one or multiple instances of time within the prediction horizon;
construct the sequence of achieving regions of states and values of the parameter vector reaching the target region of states of the ego-vehicle within the prediction horizon for the trajectory-generating function associated with the tested driving decision for the target region of states of the ego-vehicle;
construct one or multiple sequences of colliding regions of states and values of the parameter vector reaching each of the one or multiple obstacle states within the prediction horizon for the trajectory-generating function associated with the tested driving decision for each of the one or more obstacle states;
declare the tested driving decision as an admissible driving decision if there is at least one of the one or multiple values of the parameter vector for which the current state of the ego-vehicle is inside the achieving sequence of regions of states reaching the target region of states of the ego-vehicle and outside of all of the sequences of regions of states reaching each one or multiple obstacle states; and
control the ego-vehicle according to a motion model corresponding to the admissible driving decisions to reach the target region of states associated with the admissible driving decision.