Example methods and systems for controlling speeds of a vehicle may generally determine a target vehicle acceleration using an autonomy control module of the vehicle. The target vehicle acceleration may be determined based upon at least one of a target vehicle following distance, a target vehicle following speed, or a target vehicle speed. The determined vehicle acceleration may be mapped to a level of vehicle torque using a vehicle dynamics module of the vehicle. Additionally, the level of vehicle torque may be applied to one or more wheels of the vehicle.

Computer-implemented methods, systems and computer program products for facilitating operationally customized access to smart vehicles are provided. Aspects include receiving request to access a smart vehicle. Aspects also include receiving vehicle operation constraints associated with the smart vehicle using a processor. Aspects also include generating a vehicle policy based at least in part on the request to access the smart vehicle and the vehicle operation constraints using the processor. The vehicle policy includes rules for operation of the smart vehicle. Aspects also include transmitting the vehicle policy to the smart vehicle. Aspects also include moderating the operation of the smart vehicle based on at least in part the vehicle policy.

In one embodiment, a method for monitoring a localization function in an autonomous driving vehicle (ADV) can use known static objects as ground truths to determine when the localization function encounter errors. The known static objects are marked on a high definition (HD) map for the real-time driving environment. When the ADV detects one or more known static objects, the ADV can use sensor data, locations of the one or more static objects, and one or more error tolerance parameters to create a localization error tolerance area surrounding a current location of the ADV. The ADV can project the tolerance area on the HD map, performs a localization operation to generate an expected location of the ADV on the HD map, and determines whether the generated location falls within the projected tolerance area. If the generated location falls outside the projected tolerance area, indicating a localization function of the ADV encounter errors, the ADV can generate an alarm to alert a human driver to switch to a manual driving mode. If no human driver is available in the ADV, the ADV can activate a vision-based fail-safe localization procedure.

A vehicle includes a navigation configured to output navigation information; and a controller configured to receive a requested stopping location of the vehicle for a passenger to get on or off, to determine a stopping location of the vehicle based on the navigation information and information on a designated no-stopping zone, in response to the receiving of the requested stopping location, and to control the vehicle to head to the determined stopping location.

A method may include obtaining one or more inputs in which each of the inputs describes at least one of: a state of an autonomous vehicle (AV) or a state of an object; and identifying a prediction context of the AV based on the inputs. The method may also include determining a relevancy of each object of a plurality of objects to the AV in relation to the prediction context; and outputting a set of relevant objects based on the relevancy determination for each of the plurality of objects. Another method may include obtaining a set of objects designated as relevant to operation of an AV; selecting a trajectory prediction approach for a given object based on context of the AV and characteristics of the given object; predicting a trajectory of the given object using the selected trajectory prediction approach; and outputting the given object and the predicted trajectory.

A vehicle includes a controller programed to: collect a set of data related to a driver of the vehicle; predict a driving setting for the driver using the set of data and an initial student-T process (STP) machine learning (ML) model; generate an updated STP ML model based on the prediction of the driving setting as to the set of vehicle data; transmit incremental learning related to the updated STP ML model to a server; and receive, from the server, a personalized driving setting for the driver output from a cloud STP ML model trained by the incremental learning.

A vehicle and an obstacle avoidance assist method thereof are capable of performing obstacle avoidance assist control by tracking a previously sensed obstacle that deviates from a sensing region of a sensor. The obstacle avoidance assist method of a vehicle includes: detecting at least one obstacle near a vehicle using a proximity sensor; determining a travel range corresponding to a predicted travel trajectory of the body of the vehicle based on a gear stage and a steering angle; determining at least one effective obstacle, based on the travel range, from the at least one detected obstacle; and outputting a warning about the determined at least one effective obstacle.

Disclosed are an apparatus for controlling a stop of a vehicle and a method thereof. In order to prevent a collision accident occurring by a second rear vehicle in an emergency stop of the vehicle in advance, the apparatus includes a vehicle sensor that detects various types of information on a target vehicle, a rear sensor that detects a first rear vehicle and a second rear vehicle driving behind the target vehicle, and a controller that is electrically connected to the vehicle sensor and the rear sensor to control an emergency stop of the target vehicle in consideration of a state of a field of view of the second rear vehicle with respect to the target vehicle when the target vehicle is stopped in emergency.

A vehicle control apparatus, configured to control a vehicle, includes an engine that is configured to drive wheels via a power transmission device. The vehicle control apparatus includes a towing state detector and an engine controller. The towing state detector is configured to detect whether the vehicle is in a towing state. The engine controller is configured to stop the engine in a case where a predetermined engine stopping condition is satisfied during traveling of the vehicle. The engine controller is configured to vary, in a case where the towing state detector detects that the vehicle is in the towing state, the predetermined engine stopping condition to reduce an operational range in which the engine is to be stopped compared with an operational range in a case where the towing state detector does not detect that the vehicle is in the towing state.

An active driving intervention system and method based on acceleration rate optimization. After acquiring a distance from a vehicle ahead and a velocity of a driving vehicle, an evenly varying optimal acceleration variation when the driving vehicle is rapidly accelerated or rapidly decelerated in a safe condition may be calculated, which can be used to assist an accelerator or a brake in active intervention of a vehicle acceleration and appropriate adjustment of the opening of an accelerator or brake pedal, thereby realizing active intervention on driving operation. The method and system of the present disclosure can achieve the purpose of reducing energy consumption.