Apparatus for controlling a land vehicle which is self-driving or partially self-driving, which apparatus comprises a coarse tuning assembly (1, 2, 3) and a fine tuning assembly, the coarse tuning assembly (1, 2, 3) being such that it comprises: a. a sensor interface which measures kinematic parameters including speed and braking, b. fuzzy descriptions to model guidance, navigation and control of the vehicle, the fuzzy descriptions including: (i) driver behaviour and driving dynamics, (ii) uncertainties due to the environment including weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and c. an adaptive fuzzy logic controller for nonlinear multi-input multi-output systems with subsystems which comprise fuzzification, inference, and output processing, which comprise both type reduction and defuzzification, and which provide stability of a resulting closed-loop system, the adaptive fuzzy logic controller including: (i) inference engine identifying relationships using a rule base and outputs as 'fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier 'fuzzyfiying' the signal, and the fine tuning assembly being such that it comprises: a. inputs from the coarse tuning assembly (1, 2, 3), b. precognition horizons determining how many future samples an objective function considers for minimization and the length of the control sequence computed, c. a linearized multi-input multi-output regression model extracted from the adaptive fuzzy logic controller at each time step providing the 'fine' tuning parameters, and d. a non-linear dynamic linearized regression controller providing: (i) a crisp output signal feeding into artificial precognition adaptive cognised control synthesis computing the optimal future vehicle guidance, navigation and control sequence, and (ii) reduced set output and artificial precognition adaptive cognised control synthesis feeding into the artificial precgnition adaptive cognised control linear logic system. a. a sensor interface which measures kinematic parameters including speed and braking, b. fuzzy descriptions to model guidance, navigation and control of the vehicle, the fuzzy descriptions including: (i) driver behaviour and driving dynamics, (ii) uncertainties due to the environment including weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and (i) driver behaviour and driving dynamics, (ii) uncertainties due to the environment including weather, road conditions and traffic, and (iii) input faults including mechanical and electrical parts, and c. an adaptive fuzzy logic controller for nonlinear multi-input multi-output systems with subsystems which comprise fuzzification, inference, and output processing, which comprise both type reduction and defuzzification, and which provide stability of a resulting closed-loop system, the adaptive fuzzy logic controller including: (i) inference engine identifying relationships using a rule base and outputs as 'fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier 'fuzzyfiying' the signal, (i) inference engine identifying relationships using a rule base and outputs as 'fuzzy sets' to a type reducer, and (ii) output control demands including torque actuators to the fuzzyfier 'fuzzyfiying' the signal, a. inputs from the coarse tuning assembly (1, 2, 3), b. precognition horizons determining how many future samples an objective function considers for minimization and the length of the control sequence computed, c. a linearized multi-input multi-output regression model extracted from the adaptive fuzzy logic controller at each time step providing the 'fine' tuning parameters, and d. a non-linear dynamic linearized regression controller providing: (i) a crisp output signal feeding into artificial precognition adaptive cognised control synthesis computing the optimal future vehicle guidance, navigation and control sequence, and (ii) reduced set output and artificial precognition adaptive cognised control synthesis feeding into the artificial precgnition adaptive cognised control linear logic system. (i) a crisp output signal feeding into artificial precognition adaptive cognised control synthesis computing the optimal future vehicle guidance, navigation and control sequence, and (ii) reduced set output and artificial precognition adaptive cognised control synthesis feeding into the artificial precgnition adaptive cognised control linear logic system.