Abstract: A system for controlling an adaptive cruise control system can include a processor and a memory. The memory can store an override ascertainment module, an intent ascertainment module, and an intent utilization module. The override ascertainment module can include instructions that cause the processor to determine, during an operation of the adaptive cruise control system, that an action, performed by an operator of an ego vehicle, is an override of the adaptive cruise control system. The intent ascertainment module can include instructions that cause the processor to determine, during the override, an intent of the operator to change a setting of a mechanism, of the adaptive cruise control system, to control a motion-related aspect of the ego vehicle, from a current setting to a preferred setting. The intent utilization module can include instructions that cause the processor to cause the adaptive cruise control system to utilize information about the intent.

Abstract: Embodiments of methods and systems include determining a driving maneuver to be performed by an ego vehicle based on a road geometry and a lane geometry, the ego vehicle surrounded by one or more surrounding vehicles, retrieving a trajectory model corresponding to the driving maneuver, encoding kinematic data of the surrounding vehicles relative to the ego vehicle to generate spatial relationships between the surrounding vehicles and the ego vehicle, predicting future trajectories of the surrounding vehicles based on the spatial relationships, the kinematic data, the road geometry, and the lane geometry, generating a collision coefficient based on the future trajectories and the driving maneuver, and when the collision coefficient is greater than a predetermined threshold value, determining an escape route for the ego vehicle, and causing the ego vehicle to autonomously operate based on the escape route.

Abstract: An example operation includes one or more of receiving driving data of a first vehicle, receiving driving data of a second vehicle driving in a different lane than the first vehicle, determining that the first vehicle and the second vehicle will collide via execution of a machine learning model on the driving data of the first and second vehicles, and displaying a warning on a user interface associated with one or more of the first and second vehicles.

Abstract: An example operation includes one or more of receiving sensor data from one or more hardware sensors of a vehicle, obtaining multi-modal data of an occupant of the vehicle from the sensor data, predicting, via a machine learning model, the occupant of the vehicle suffers from attention deficit hyperactivity disorder (ADHD) based on the multi-modal data, and causing one or more hardware systems within the vehicle to provide an audible alert to the occupant based on the prediction.

Abstract: An example operation includes one or more of obtaining image data of an occupant of a vehicle from one or more hardware sensors associated with the vehicle, extracting occupant data of the occupant from the obtained image data, predicting that the occupant is impaired from cannabis based on execution of a machine learning model on the extracted occupant data, and transmitting an alert to the vehicle based on the prediction.

Abstract: An example operation includes one or more of receiving position data of an ego vehicle and of one or more surrounding vehicles of the ego vehicle via sensors of the ego vehicle while the ego vehicle and the surrounding vehicles are travelling along a road, predicting, via a machine learning model, a trajectory of the ego vehicle and trajectories of the one or more surrounding vehicles based on the position data, generating a routing instruction for the ego vehicle based on the predicted trajectory of the ego vehicle and the predicted trajectories of the one or more surrounding vehicles, and transmitting the routing instruction to a display associated with the ego vehicle.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a vehicle when the vehicle is traveling along a road behind a lead vehicle, determining a size of the lead vehicle, predicting, via execution of a machine learning model, a recommended gap distance of the vehicle between the vehicle and the lead vehicle based on the obtained sensor data and the determined size, and notifying the vehicle of the recommended gap distance.

Abstract: An example operation includes one or more of obtaining sensor data captured by one or more sensors of a transport while the transport is traveling behind a lead transport, detecting that a gap distance between the transport and the lead transport is outside a predetermined threshold based on the obtained sensor data, in response to the detection, determining a recommended gap distance between the transport and the lead transport, and controlling, via the server, a speed of the transport via an activated cruise control function based on the recommended gap distance.