There is provided a portable electronic monitoring device for providing an in-vehicle user warning system about how a semi-autonomous vehicle is being driven autonomously during a driving period. The device is removably and securely mountable to the vehicle and comprises: a sensor set comprising at least one sensor for sensing an exterior environment outside of the vehicle and movement of the vehicle within the exterior environment, an interface for receiving user input commands and delivering a warning output; and a processor operatively connected to the sensor set and the interface; wherein the sensor set is configured to monitor the automatic operation of the semi-autonomous vehicle within the exterior environment during the driving period and to generate sensor data representing driving events concerning the automated driving behaviour of the vehicle with respect to the exterior environment occurring during the driving period. The processor is configured to: process the sensor data during the driving period to compare the detected automated driving behaviour of the vehicle in the external environment with a model of expected automated vehicle driving behaviour for a particular driving event; identify a dangerous driving event, if the detected automated driving behaviour deviates beyond a threshold from the expected automated vehicle driving behaviour; and if a dangerous driving event has been detected, generate a warning alert via the interface to alert the driver to the occurrence of the dangerous driving event.

An illustrative example method is for estimating a friction characteristic of a surface beneath a vehicle that has a plurality of wheels contacting the surface. The method includes determining a wheel speed of at least one of the wheels, determining a velocity of the at least one of the wheels separately from determining the wheel speed, determining a wheel slip of the at least one of the wheels based on the determined wheel speed and the determined velocity, and determining the friction characteristic based on the determined wheel slip. Determining the velocity separately from the wheel speed is accomplished using at least one detector that provides an output corresponding to a range rate, such as a RADAR or LIDAR detector.

Methods are provided for controlling various systems in a vehicle based on input signals from at least one physical sensor and at least one model of a vehicle or a portion of the vehicle. The controller a rely preferentially on one or the other inputs based on the frequency of a motion of the vehicle and the state of the vehicle or one or mor portions of the vehicle.

A smooth cooperative lane change control method for multi-connected and autonomous vehicles (CAVs), including: acquiring vehicle information of a lane-changing vehicle M and four surrounding vehicles A, B, C and D; constructing uncontrolled-vehicle and controlled-vehicle motion state prediction models; according to the motion state prediction models, predicting motion states of the lane-changing vehicle M, and the vehicles A, B, C and D; constructing an upper-layer optimization model to calculate an optimal control value and an optimized motion state of the lane-changing vehicle M and an optimal control value of the vehicle A; constructing a lower-layer optimization model to calculate an optimal control value of the vehicle D; and controlling the lane-changing vehicle M, the vehicle A and the vehicle D to run according to a corresponding optimal control value.

A controller may indicate a low-traction mode of a vehicle when a longitudinal tracking accumulation exceeds a first threshold value and a lateral response accumulation exceeds a second threshold value. The longitudinal tracking accumulation may measure a tally of activation of a traction control system over time. The lateral response accumulation may measure a comparison of the vehicle yaw-rate to a driver-desired model-based prediction of the yaw-rate. The controller may indicate the low-traction mode by providing a recommendation to switch to the low-traction mode in a human-machine interface screen of the vehicle, or by automatically adjusting the operational mode of at least one electronic control unit of the vehicle to implement the low-traction mode.

A computer-implemented method for training a machine learning system for generating driving profiles and/or driving routes of a vehicle including: a generator obtains first random vectors and generates first driving routes and associated first driving profiles related to the first random vectors, driving routes and respectively associated driving profiles recorded in driving mode are stored in a data base, second driving routes and respectively associated second driving profiles recorded in driving mode are selected from the database, a discriminator obtains first pairs made up of first generated driving routes and respectively associated first generated driving profiles and second pairs made up of second driving routes and respectively associated second driving profiles recorded in driving mode, the discriminator calculates outputs that characterize each pair, and a target function is optimized as a function of the outputs of the discriminator.

An autonomous system may selectively displace human driver control of a host vehicle. The system may receive an image representative of an environment of the host vehicle and detect an obstacle in the environment of the host vehicle based on analysis of the image. The system may monitor a driver input to a throttle, brake, and/or steering control associated with the host vehicle. The system may determine whether the driver input would result in the host vehicle navigating within a proximity buffer relative to the obstacle. If the driver input would not result in the host vehicle navigating within the proximity buffer, the system may allow the driver input to cause a corresponding change in one or more host vehicle motion control systems. If the driver input would result in the host vehicle navigating within the proximity buffer, the system may prevent the driver input from causing the corresponding change.

A controller includes a processor programmed to determine, for a vehicle, a first control input based on input data and first reference parameters. The processor is further programmed to operate the vehicle according to the first control input. Based on operating data of the vehicle for an operating condition, the processor determines a second control input for the vehicle. Operating the vehicle according to the second control input reduces a cost of operating the vehicle relative to operating the vehicle according to the first control input. The processor is further programmed to determine, based on the second control input, second reference parameters. The controller generates a third control input based on the second reference parameters and the input data. A cost of operating the vehicle according to the third control input is reduced relative to the cost of operating the vehicle based on the first control input.

An autonomous vehicle is provided that includes one or more sensors coupled to the autonomous vehicle, and a computing device configured to: (i) receive, from the one or more sensors, operational data related to an operation of the autonomous vehicle, (ii) receive geographical data related to an anticipated route of the autonomous vehicle, (iii) generate, for the anticipated route and based on the operational data and the geographical data, an operational response model representing respective operational constraints for one or more operational parameters of the autonomous vehicle, wherein values for the one or more operational parameters are represented along coordinate axes of a geometrical shape, and wherein the one or more operational parameters are mutually coupled to each other, and (iv) responsively execute, based on the operational response model, an autonomous control strategy comprising one or more adjustments to the operation of the vehicle within the respective operational constraints.

Disclosed are a vehicle for generating an acceleration profile based on the acceleration cognitive characteristics of the human and a method of controlling the same. The method includes: receiving a manipulation amount of an accelerator pedal and calculating a first torque value, inserting the first torque value to a function that receives force and outputs acceleration feeling, generating a second torque value by inserting an output value of the function into a first filter for stabilizing the output value, generating a target torque value by inputting the second torque value to a second filter for stabilizing the second torque value, and generating a torque command based on the target torque value.