System, methods, and other embodiments described herein relate to improving event management in a vehicle by aggregating information sources. In one embodiment, a method includes selecting unfiltered events, from a stream of events that are classified and filtered, and a display medium according to a decision policy associated with an operator. The method also includes communicating the unfiltered events selected for the display medium. The method also includes adapting the decision policy according to collected tracking information of operator attention associated with the unfiltered events and reward values associated with the tracking information.

Systems and methods are described herein for managing communications for a connected vehicle, such as between the connected vehicle and other connected vehicle and/or between the connected vehicle and infrastructure entities, such as providers of services to the connected vehicle. For example, a communication network, such as a network provided by a network carrier, may include various cloud engines or other network-based servers that manage, coordinate, and/or provision communications between the connected vehicle and other parties, such as vehicles, road devices, buildings, and other infrastructure entities.

One or more braking event detection computing devices and methods are disclosed herein based on fused sensor data collected during a window of time from various sensors of a mobile device found within an interior of a vehicle. The various sensors of the mobile device may include a GPS receiver, an accelerometer, a gyroscope, a microphone, a camera, and a magnetometer. Data from vehicle sensors and other external systems may also be used. The braking event detection computing devices may adjust the polling frequency of the GPS receiver of the mobile device to capture non-consecutive data points based on the speed of the vehicle, the battery status of the mobile device, traffic-related information, and weather-related information. The braking event detection computing devices may use classification machine learning algorithms on the fused sensor data to determine whether or not to classify a window of time as a braking event.

An automotive adaptive cruise control system for a host motor vehicle configured to operate in at least two different operating modes comprising a first operating mode, in which a current speed of the host vehicle is controlled to maintain a cruise speed, and a second operating mode, in which the current speed of the host vehicle is controlled to maintain a cruise distance to a leading vehicle, wherein the system is configured to: detect approaching to a traffic light and determine a light signal emitted thereby, signal to the driver the presence of the detected traffic light and the determined light signal, if the traffic light emits a red or amber light signal, estimating a driver reaction time, determining a higher threshold distance and a lower threshold distance from the traffic light, and warning the driver of the host vehicle of the need to slow it down if, after the driver reaction time has elapsed: i) the host motor vehicle has not decreased its speed by more than a calibratable threshold, ii) the current speed of the host vehicle is higher than a minimum speed, iii) either the distance of the host vehicle from the traffic light is lower than the higher threshold distance and the light signal emitted by the traffic light is red, or the distance of the host vehicle from the traffic light is between the higher and lower threshold distances and the light signal emitted by the traffic light is amber, and iv) a service brake of the host vehicle is unoperated.

A travel control apparatus controls automated driving travel of a vehicle by making a transition between a plurality of control states based on a surrounding environment of the vehicle. The travel control apparatus includes a skill level acquisition unit configured to acquire a skill level of a vehicle occupant about automated driving, and a control unit configured to control the automated driving travel of the vehicle in one of the plurality of control states based on the surrounding environment of the vehicle sensed by a sensing unit.

A vehicle includes an internal camera configured to acquire the driver's facial data to identify the driver's gaze region; an external sensor configured to acquire object data including at least one of a relative position and a relative speed of the object existing in all directions of the vehicle; and a controller configured to generate an advance warning signal.

The present disclosure is to evaluate a driving ability of a driver. A system includes a vehicle and an information processing apparatus. The vehicle detects rush-out of a moving object with a possibility of colliding with the vehicle by a sensor mounted on the vehicle, and also detects that a driver of the vehicle takes an evasive operation for avoiding a collision with the moving object. The information processing apparatus obtains a first time point, which is a point in time at which the vehicle detects the rush-out of the moving object, and a second time point, which is a point in time at which the vehicle detects that the driver takes an evasive action, calculates a reaction time that is a time difference between the first time point and the second time point, and evaluates the driving ability of the driver based on the reaction time.

A system can train a vehicle electronically to accommodate a driver. The system can train the vehicle to accommodate the ability, condition, and/or personality of the driver. The system can change the controls of the vehicle, responsive to the inputs from the driver, to match with the patterns of controls resulting from a predetermined model (such as a safe-driver model). Accordingly, the vehicle can appear as it is being driven by a safe driver when it may not be the case. A driver with a lower driving competence may apply physical controls in a pattern that may be slow, unstable, or insufficient. However, the vehicle can be trained to adjust the transformation from the UI signals to the drive-by-wire signals such that the transformed signals appear to be applied by a more competent driver on the road. And, the transformation can improve over time with training via machine learning.

A test system for detecting impairment aboard a motor vehicle includes an electronic control unit (ECU) and sensors in communication therewith. The sensors are positioned within a vehicle interior, and include at least a touch screen and a microphone, and possibly an eye-tracking camera. In response to receiving a start request indicative of a requested start event of the motor vehicle, the ECU executes instructions to initiate a cognitive response test via the sensors. The ECU determines a test score of a driver during the test while the motor vehicle remains off, compares the test score to baseline scores to determine a passing or failing test result, and executes a control action aboard the motor vehicle in response to the passing or failing test result. The ECU may disable or immobilize the motor vehicle in response to the failing test result.

A vehicle system is provided. The vehicle system includes a first driving input device, a second driving input device, and an autonomous vehicle control module. The autonomous vehicle control module is configured to monitor a driving behavior of a first driver operating the first driving input and to automatically transfer vehicle control of a vehicle from the first driver to the second driver when one or more requirements are met based on the driving behavior of the first driver. The autonomous vehicle control module is further configured to analyze switching of vehicle control between the first driver and the second driver.