An example operation includes one or more of determining one or more objects in a transport that may become airborne and altering a portion of the transport to minimize the one or more objects from becoming airborne during a collision.

A control unit for controlling a driving function of a vehicle is designed to automatically guide the vehicle longitudinally and/or transversely. The control unit is designed to determine that the driver of the vehicle is presently activating or deactivating, and/or intends to activate or deactivate, the driving function. In response thereto, the control unit is additionally designed to cause a manual control intervention produced by the driver of the vehicle in the longitudinal and/or transversal guidance of the vehicle to be at least partly compensated for and/or suppressed prior to the point in time of the activation or deactivation of the driving function in order to adapt the drive behavior of the vehicle during the transition between the manual longitudinal and/or transversal guidance and the automatic longitudinal and/or transversal guidance.

This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

One or more outputs from a planning module of an ADV are received. Data of a driving environment of the ADV is received. A performance of the planning module is evaluated by determining a score of the performance of the planning module based on the data of the driving environment and the one or more outputs from the planning module. Whether the one or more outputs from the planning module violates at least one of a set of safety rules is determined. The score is determined being larger than a predetermined threshold in response to determining that the one or more outputs from the planning module violate at least one of the set of safety rules. Otherwise, the score is determined based on a machine learning model. The planning module is modified by tuning a set of parameters of the planning module based on the score.

Provided is a navigation system for a leader vehicle leading follower vehicles, including: the leader vehicle, configured to transmit, real-time movement data to follower vehicles; and, the follower vehicles, each comprising: a signal receiver for receiving the data from the leader vehicle; sensors configured to detect at least one maneuverability condition; a memory; a vehicle maneuver controller; a distance sensor; and a processor configured to: determine a route for navigating the local follower vehicle from an initial location; determine a preferred range of distances from the vehicle in front of the respective follower vehicle that the respective follower vehicle should stay within; determine a set of active maneuvering instructions for the respective follower vehicle based on at least a portion of the data received from the guiding vehicle; determine a lag in control commands; and, execute the set of active maneuvering instructions in the respective follower vehicle.

A vehicle may receive sensor data captured by a sensor of the vehicle, determine that the sensor data represents an object in the environment, and determine an impact location between the vehicle and the object. The impact location may be associated with a predicted collision between the vehicle and the object. The vehicle may also determine an object type corresponding to the object and/or a characteristic of the object. Based at least in part on the impact location, object type, and/or the characteristic, a ride height of the vehicle may be adjusted.

In one embodiment, when an autonomous driving vehicle (ADV) is parked, the ADV can determine, based on criteria, whether to operate in an open-space mode or an on-lane mode. The criteria can include whether the ADV is within a threshold distance and threshold heading relative to a vehicle lane. If the criteria are not satisfied, then the ADV can enter the open-space mode. While in the open-space mode, the ADV can maneuver it is within the threshold distance and the threshold heading relative to the vehicle lane. In response to the criteria being satisfied, the ADV can enter and operate in the on-lane mode for the ADV to resume along the vehicle lane.

Techniques are provided for autonomous vehicle operation using linear temporal logic. The techniques include using one or more processors of a vehicle to store a linear temporal logic expression defining an operating constraint for operating the vehicle. The vehicle is located at a first spatiotemporal location. The one or more processors are used to receive a second spatiotemporal location for the vehicle. The one or more processors are used to identify a motion segment for operating the vehicle from the first spatiotemporal location to the second spatiotemporal location. The one or more processors are used to determine a value of the linear temporal logic expression based on the motion segment. The one or more processors are used to generate an operational metric for operating the vehicle in accordance with the motion segment based on the determined value of the linear temporal logic expression.

Systems and methods are provided for implementing hybrid prediction. Hybrid prediction integrates two deep learning based trajectory prediction approaches: grid-based approaches and graph-based approaches. Hybrid prediction techniques can achieve enhanced performance by combining the grid and graph approaches in a manner that incorporates appropriate inductive biases for different elements of a high-dimensional space. A hybrid prediction framework processor can generate trajectory predictions relating to movement of agents in a surrounding environment based on a prediction model generating using hybrid prediction. Trajectory predictions output from the hybrid prediction framework processor can be used to control an autonomous vehicle. For example, the autonomous vehicle can perform safety-aware and autonomous operations to avoid oncoming objects, based on the trajectory predictions.

The present disclosure relates to a vehicle and control method thereof, to a vehicle having a driver assistance system for assisting a driver. When a lane change is requested even though it does not meet the predetermined lane change condition, present disclosure provides a vehicle driver assistance system (ADAS) that can actively indicate a lane change intention to an adjacent vehicle through ‘deflected driving in a lane’ and perform lane change safely after confirming the yield/overtake intention of the adjacent vehicle. It is an aspect of the present disclosure to provide a control method of a vehicle, including: confirming whether the surrounding situation of the vehicle satisfies a lane change condition when a lane change command occurs while the vehicle is driving autonomously; performing deflected driving in the lane of the vehicle to indicate a lane change intention when the surrounding situation of the vehicle does not satisfy the lane change condition; and performing a lane change corresponding to the lane change command when the yield intention for the lane change intention is confirmed from another vehicle around the traveling lane after indicating the lane change intention through the deflected driving.