In one embodiment, a method is provided. The method includes determining a first reference line representing a path through an environment for an autonomous driving vehicle. The method also includes determining a speed constraint function based on a set of speed limits associated with the environment, a set of curvatures of the path, and a set of obstacles in the environment, wherein the speed constraint function comprises a continuous function. The method further includes determining a set of speeds for the path through the environment based on the speed constraint function. The method further includes controlling the autonomous driving vehicle based on the path and the set of speeds.

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.

Techniques for cognitive analysis for directed control transfer with autonomous vehicles are described. In-vehicle sensors are used to collect cognitive state data for an individual within a vehicle which has an autonomous mode of operation. The cognitive state data includes infrared, facial, audio, or biosensor data. One or more processors analyze the cognitive state data collected from the individual to produce cognitive state information. The cognitive state information includes a subset or summary of cognitive state data, or an analysis of the cognitive state data. The individual is scored based on the cognitive state information to produce a cognitive scoring metric. A state of operation is determined for the vehicle. A condition of the individual is evaluated based on the cognitive scoring metric. Control is transferred between the vehicle and the individual based on the state of operation of the vehicle and the condition of the individual.

A vehicle control device includes at least one electronic control unit configured to recognize at least one object, calculate a time to collision, operate first driving assistance, when the time to collision is equal to or less than a first threshold value, operate second driving assistance for avoiding the collision between the at least one object and the host vehicle or reducing damage of the collision, when the time to collision is equal to or less than a second threshold value smaller than the first threshold value, and set, while the first driving assistance is operated, the second threshold value to a second setting value smaller than a first setting value set when the first driving assistance is not operated, when a second target object causing the second driving assistance to operate is the same object as a first target object causing the first driving assistance to operate.

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.

In a network of autonomous or semi-autonomous vehicles, an alert may be triggered when one of the vehicles switches from autonomous to manual mode. The alert may be communicated to nearby autonomous vehicles so that drivers of those vehicles may become aware of a potentially unpredictable manual driver nearby. Drivers of autonomous vehicles who may have become disengaged (e.g., sleeping, reading, talking, etc.) during autonomous driving may become re-engaged upon noticing the alert. A re-engaged driver may choose to switch his/her own vehicle from autonomous to manual mode in order to appropriately react to an unpredictable nearby manual driver. In additional or alternative embodiments, the alert may be triggered or intensified when indications of impairment of a nearby driver or malfunction of a nearby vehicle are detected.

Methods are described that perform a dispatched consumer-to-store return or swap logistics operation for an item being replaced using a modular autonomous bot apparatus assembly and a dispatch server. The method begins with receiving a return operation dispatch command that includes identifier information, transport parameters, and designated pickup information for the item being replaced/returned, along with authentication information related to an authorized supplier of the item being replaced. Modular components of the bot apparatus are verified to be compatible with the dispatched logistics operation. The MAM then autonomously causes the bot apparatus to move to the designated pickup location, notifies the authorized supplier of an approaching pickup, receives supplier authorization input to permissively allow access to a payload area within the bot apparatus, monitors loading as the item being replaced is received along with return documentation, and then autonomously causes movement of the bot apparatus back to the origin location.

An electronic device obtains location information and traveling status information of a vehicle terminal and road condition information of a road section on which the vehicle terminal travels. The electronic device obtains blind spot detection capability information of the vehicle terminal. The electronic device determines a potential blind spot of the vehicle terminal in a traveling process according to the location information and the traveling status information of the vehicle terminal and the road condition information to obtain a blind spot detection result. The electronic device generates a blind spot supplementary reminder message according to the blind spot detection result and the blind spot detection capability information of the vehicle terminal, and sends the blind spot supplementary reminder message to the vehicle terminal.

In one embodiment, a method of adjusting a speed limit of an ADV includes the operations of tracking objects within a field of view of the ADV; and identifying a set of stable objects from the objects tracked by the ADV based on a set of requirements. The method further includes the operations of identifying a subset of objects from the set of stable objects, the subset of objects having longest distances to the ADV; calculating a detection distance by averaging distances from the subset of stable obstacles to the ADV; and adjusting the speed limit of the ADV based on the detection distance using a predetermined algorithm.

A device for searching a parking space includes at least one space detection sensor mounted on a vehicle, and a controller that analyzes sensed information obtained through the space detection sensor to recognize space and object information within a parking lot, predicts a distribution of available parking spaces in the parking lot based on the recognized space and object information, and determines an optimal available parking space based on the distribution of the available parking spaces and characteristics of a driver.