Methods and systems for adapting an advanced driver assistance driving system of a vehicle. One system includes an electronic processor of an advanced driver assistance driving system. The electronic processor is configured to control the vehicle using a control parameter of the advanced driver assistance driving system. The electronic processor is also configured to activate a learning mode for the advanced driver assistance driving system and receive feedback associated with the control of the vehicle. The electronic processor is also configured to adjust the control parameter of the advanced driver assistance driving system based on the feedback and control the vehicle using the adjusted control parameter.

Embodiments herein can determine an optimal route for an autonomous electric vehicle. The system may score viable routes between the start and end locations of a trip using a numeric or other scale that denotes how viable the route is for autonomy. The score is adjusted using a variety of factors where a learning process leverages both offline and online data. The scored routes are not based simply on the shortest distance between the start and end points but determine the best route based on the driving context for the vehicle and the user.

Embodiments herein relate to an autonomous vehicle or self-driving vehicle. The system can determine a collision avoidance path by: 1) predicting the behavior/trajectory of other moving objects (and identifying stationary objects); 2) given the driving trajectory (issued by autonomous driving system) or predicted driving trajectory (human), establishing the probability for a collision that can be calculated between the vehicle and one or more objects; and 3) finding a path to minimize the collision probability.

Methods for management of a powertrain system in a vehicle. The methods receive data or signals from multiple sensors associated with the vehicle. Optimum thresholds for classifications of the sensor data can be changed based injecting signals into the powertrain system and receiving responsive signals. Expected priorities for the sensor signals can be altered based upon attributes of the signals and confirming actual priorities for the signals. Look-up tables for engine management can be modified based upon injecting signals into the powertrain system and measuring a utility of the responsive signals. The methods can thus dynamically alter and modify data for powertrain management, such as look-up tables, during vehicle operation under a wide range of conditions.

A mobile object according to an embodiment of the present technology includes an acquisition unit, a detection unit, and a determination unit. The acquisition unit acquires situation information regarding a situation of the mobile object. The detection unit detects an instability element for autonomous traveling control of the mobile object on the basis of the acquired situation information. The determination unit determines a control method for executing the autonomous traveling control on the basis of the detected instability element.

Systems and methods described herein relate to predicting a trajectory of a vehicle. One embodiment generates first and second predicted vehicle trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past trajectory information and vehicle sensor data; generates a confidence score for each of the first and second predicted vehicle trajectories using a confidence estimator that includes a first deep neural network, wherein generating the confidence scores includes computing the confidence scores as a function of time within a predetermined temporal horizon; outputs the first and second predicted vehicle trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted vehicle trajectory, the second predicted vehicle trajectory, the confidence score for the first predicted vehicle trajectory, and the confidence score for the second predicted vehicle trajectory.

Systems and methods described herein relate to predicting a trajectory of a road agent external to a vehicle. One embodiment generates first and second predicted road-agent trajectories using respective first and second trajectory predictors based, at least in part, on a plurality of inputs including past road-agent trajectory information and vehicle sensor data; generates a confidence score for each predicted road-agent trajectory using a confidence estimator that includes a deep neural network, wherein generating the confidence scores includes computing them as a function of time within a predetermined temporal horizon; outputs the first and second predicted road-agent trajectories and their respective confidence scores; and controls operation of the vehicle based, at least in part, on one or more of the first predicted road-agent trajectory, the second predicted road-agent trajectory, the confidence score for the first predicted road-agent trajectory, and the confidence score for the second predicted road-agent trajectory.

Systems and methods described herein relate to controlling the operation of a vehicle. One embodiment generates predicted trajectories of the vehicle using first trajectory predictors based, at least in part, on first inputs; generates predicted trajectories of a road agent that is external to the vehicle using second trajectory predictors based, at least in part, on second inputs; integrates the predicted trajectories of the road agent into the first inputs to iteratively update the predicted trajectories of the vehicle and integrates the predicted trajectories of the vehicle into the second inputs to iteratively update the predicted trajectories of the road agent; and controls operation of the vehicle based, at least in part, on at least one of (1) the iteratively updated predicted trajectories of the vehicle and (2) the iteratively updated predicted trajectories of the road agent.

The present disclosure relates to systems and methods for autonomous driving. The systems may obtain driving information associated with a vehicle; determine a state of the vehicle; determine one or more candidate control signals and one or more evaluation values corresponding to the one or more candidate control signals based on the driving information and the state of the vehicle by using a trained control model; select a target control signal from the one or more candidate control signals based on the one or more evaluation values; and transmit the target control signal to a control component of the vehicle.

The disclosure relates to a hybrid vehicle and a method of controlling of the hybrid vehicle, and an aspect of the disclosure is to generate optimal vehicle control values through learning using Q-learning technique of reinforcement learning in the field of machine learning based on vehicle state information. The method of controlling the hybrid vehicle includes obtaining vehicle state information including battery SOC information, engine on/off information, demand power, vehicle speed information, and fuel consumption information; creating a vehicle model information map using the vehicle state information; creating a Q value table based on the vehicle model information map; and calculating power distribution control values of an engine and a motor through reinforcement learning based on the Q value table.