Predictive semi-autonomous vehicle navigation system

Abstract

An active safety framework performs trajectory planning, threat assessment, and semi-autonomous control of passenger vehicles in a unified, optimal fashion. The vehicle navigation task is formulated as a constrained optimal control problem. A predictive, model-based controller iteratively plans an optimal or best-case vehicle trajectory through the constrained corridor. This best-case scenario is used to establish the minimum threat posed to the vehicle given its current state, current and past driver inputs/performance, and environmental conditions. Based on this threat assessment, the level of controller intervention required to prevent collisions or instability is calculated and driver/controller inputs are scaled accordingly. This approach minimizes controller intervention while ensuring that the vehicle does not depart from a traversable corridor. It also provides a unified architecture into which various vehicle models, actuation modes, trajectory-planning objectives, driver preferences, and levels of autonomy can be seamlessly integrated without changing the underlying controller structure.

1 . A method for generating a set of machine control inputs for semi-autonomously controlling a vehicle operating in an environment, with a variable degree of human operator control relative to the degree of machine control, the method comprising the steps of:
 a. predicting an optimal vehicle trajectory from a current position through a time horizon:   b. assessing a predicted threat to the vehicle and generating a corresponding threat metric;   c. based on the threat metric, generating at least one control authority gain; and   d. generating at least one machine control optimal input; and   e. generating at least one machine control scaled input, based on the machine control optimal input and the control authority gain;   whereby the degree of machine control of the vehicle relative to the degree of human operator control of the vehicle varies depending on the control authority gain.   
     
     
         2 . The method of  claim 1 , the step of predicting an optimal vehicle trajectory being based on:
 a. a model of the environment;   b. a model of the vehicle;   c. the vehicle's current state;   d. driver inputs; and   e. a corresponding optimal set of control inputs;   
     
     
         3 . The method of  claim 1 , the step of assessing a predicted threat being based on:
 a. characteristics of optimal vehicle path and associated control input;   b. environmentally imposed safety constraints; and   c. and driver inputs.   
     
     
         4 . The method of  claim 1 , the step of generating at least one machine control scaled input being based on an intervention characteristic. 
     
     
         5 . The method of  claim 4 , the intervention characteristic being chosen from the group consisting of a linear function of current and past predicted threat, and current and past control input; and a nonlinear function of current and past predicted threat, and current and past control input. 
     
     
         6 . The method of  claim 1 , the environmental model being based on a priori known information. 
     
     
         7 . The method of  claim 1 , the environmental model being based on information gathered by real-time sensors. 
     
     
         8 . The method of  claim 1 , the threat metric being at least one metric selected from the group consisting of:
 maximum lateral acceleration, sideslip angle, roll angle over the trajectory and a minimum proximity to obstacles.   
     
     
         9 . The method of  claim 1 , the optimal vehicle trajectory and associated optimal control inputs being computed by constrained optimal control. 
     
     
         10 . The method of  claim 3 , the vehicle comprising an automotive vehicle, with at least one sensor generating data related to at least one of the factors in the group consisting of: nearby vehicles, pedestrians, road edges, roadway hazards, road surface friction and other environmental characteristics. 
     
     
         11 . The method of  claim 1 , control authority gain being such that if the threat metric value is:
 a. low, the control system intervention is low and thus, the human operator controls the vehicle with minimal computer-controlled intervention); and   b. high, the control system intervention is high and thus, the human operator controls the vehicle with significant computer controlled intervention.   
     
     
         12 . The method of  claim 1 , the vehicle comprising an automotive vehicle, at least one machine control scaled input being selected from the group consisting of: steering, braking and acceleration. 
     
     
         13 . The method of  claim 1 , the optimal set of machine control inputs used in the step of predicting an optimal vehicle trajectory comprising machine control inputs having been generated by the method for generating a set of automated control inputs. 
     
     
         14 . An apparatus for use of generating a set of machine control inputs thereby controlling a vehicle operating in an environment, with a variable degree of human operator control and a variable degree of machine control, the apparatus comprising:
 a. means for predicting an optimal safe vehicle trajectory from a current position through a time horizon based on;
 i. a model of the environment; 
 ii. a model of the vehicle; 
 iii. the vehicle's current state; 
 iv. driver inputs; and 
 v. a corresponding optimal set of control inputs; 
   b. means for assessing a predicted threat to the vehicle and generating a corresponding threat metric;   c. a machine controller that generates at least one machine control optimal input;   d. means for generating at least one control authority gain based on the threat metric;   e. means for generating at least one machine control scaled input based on the at least one machine control optimal input and the control authority gain;   f. means for generating a scaled human operator input, based on a human operator command, and the control authority, whereby the human operator scaled input is also based on the control authority gain, inversely to the degree that the machine control scaled input is based on the at least one machine control optimal input; and   g. an input combiner, which combines the human operator scaled input and the machine control scaled input to an actuator that actuates a system of the vehicle.   
     
     
         15 . An automotive vehicle having a chassis, wheels, a power plant, a body, and a control apparatus, the control apparatus generating a set of machine control inputs thereby controlling the vehicle while operating in an environment, with a variable degree of human operator control and a variable degree of machine control the control apparatus comprising:
 a. means for predicting an optimal safe vehicle trajectory from a current position through a time horizon based on;
 i. a model of the environment; 
 ii. a model of the vehicle; 
 iii. the vehicle's current state; 
 iv. driver inputs; and 
 v. a corresponding optimal set of control inputs; 
   b. means for assessing a predicted threat to the vehicle and generating a corresponding threat metric;   c. a machine controller that generates at least one optimal machine control input;   d. means for generating at least one control authority gain based on the threat metric;   e. means for generating at least one machine control scaled input based on the at least one machine control optimal input and the control authority gain;   f. means for generating a scaled human operator input, based on a human operator command, and the control authority, whereby the human operator scaled input is also based on the control authority gain, inversely to the degree that the machine control scaled input is based on the at least one optimal control input; and   g. an input combiner, which combines the human operator scaled input and the machine control scaled input to an actuator that actuates a system of the vehicle.   
     
     
         16 . The method of  claim 1 , the threat metric being at least one metric selected from the group consisting of:
 characteristics of the optimal vehicle path and control input, including predicted vehicle states of lateral acceleration, vehicle sideslip angle, tire sideslip angle, road friction utilization, roll angle, pitch angle, past and present driver performance, environmentally-imposed safety constraints, and proximity to hazards.   
     
     
         17 . The method of  claim 16 , the threat metric being based on at least one metric selected from the group consisting of:
 average, maximum, minimum, and RMS norms of a predicted vehicle state.   
     
     
         18 . The method of  claim 17 , the predicted vehicle state being selected from the group consisting of: lateral acceleration, vehicle sideslip angle, tire sideslip angle, road friction utilization, roll angle, pitch angle, driver inputs, and proximity to hazards.