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.

Disclosed herein are an apparatus and a method for autonomous vehicle negotiation based on Vehicle-to-Vehicle (V2V) communication, the method including requesting, by vehicles that enter a driving negotiation section, a driving negotiation token, acquiring, by a vehicle that enters the driving negotiation section first, among the entering vehicles, the driving negotiation token, performing driving negotiation based on whether the driving negotiation token is acquired, and returning, by a vehicle having acquired the driving negotiation token, the driving negotiation token when the vehicle arrives at a destination.

A method of operating an autonomous vehicle includes determining, by the autonomous vehicle, whether a target is in an intended maneuver zone around the autonomous vehicle; generating, by the autonomous vehicle, a signal in response to determining that the target is within the intended maneuver zone around the autonomous vehicle; determining, by the autonomous vehicle and based on perception information acquired by the autonomous vehicle, whether the target has left the intended maneuver zone around the autonomous vehicle; and determining, by the autonomous vehicle, that it is safe to perform the intended maneuver in response to determining, by the autonomous vehicle, that the target is not in the intended maneuver zone or in response to determining, by the autonomous vehicle, that the target has left the intended maneuver zone.

Connected and automated vehicles (CAVs) have shown the potential to improve safety, increase road throughput, and optimize energy efficiency and emissions in several complicated traffic scenarios. This invention describes a mixed-integer programming (MIP) optimization method for global multi-vehicle decision making and motion planning of CAVs in a highly dynamic environment that consists of multiple human-driven, i.e., conventional or manual, vehicles and multiple conflict zones, such as merging points and intersections. The proposed approach ensures safety, high throughput and energy efficiency by solving a global multi-vehicle constrained optimization problem. The solution provides a feasible and optimal time schedule through road segments and conflict zones for the automated vehicles, by using information from the position, velocity, and destination of the manual vehicles, which cannot be directly controlled. Despite MIP having combinatorial complexity, the proposed formulation remains feasible for real-time implementation in the infrastructure, such as in mobile edge computers (MECs).

An autonomous vehicle includes an array of sensors, a processor, and a switch. The array of sensors generate sensor data related to one or more objects in an external environment of the autonomous vehicle and the processor determines an environmental context. The switch transfers the sensor data from the array of sensors to the processor, where the switch is configured to: (a) receive first sensor data from a first sensor group of the array of sensors; (b) receive second sensor data from a second sensor group of the array of sensors; (c) determine an order of transmission of the first sensor data over the second sensor data in response to the environmental context; and (d) transmit the first sensor data to the processor prior to transmitting the second sensor data based on the order of transmission.

A system and method of detecting and mitigating an erratic vehicle by a host vehicle. The method includes gathering sensor information on a calibratable external region surrounding the host vehicle; analyzing the sensor information to detect a target vehicle traveling in a lane and a movement of the target vehicle in the lane; determining whether the movement of the target vehicle in the lane is erratic; if erratic then designating target vehicle as erratic vehicle; assigning a risk score to the erratic vehicle; and implementing a predetermined mitigating action correlating to the assigned risk score to the erratic vehicle. The mitigating action includes one or more of: warning an operator of the host vehicle, warning a vehicle proximal to the host vehicle, and taking at least partial control of the host vehicle to further distance the host vehicle apart from the erratic vehicle.

A method comprises receiving, at a vehicle system of a vehicle, sensor data from one or more sensors; detecting an object based on the sensor data; determining whether a dynamic map maintained by a remote computing device includes the detected object; upon determination that the dynamic map includes the detected object, determining a remaining time-to-live associated with the detected object based on data associated with the dynamic map; and transmitting data about the detected object to the remote computing device if the determined remaining time-to-live associated with the detected object is less than a predetermined threshold time.

A voice control system for recreational vehicles controls safe operation of deployable components of the RV, such as an antenna, awning or room extension. A voice recognition system is employed to interpret voice commands and control operation of the deployable components in response. A control system monitors the status of the RV and its components to detect unsafe conditions relating to operation of the deployable components. For example, this can be done via a wired or wireless network of sensors detecting the state of the RV and its deployable components. The control system can also monitor the status of the RV via the data bus built into the RV. If an unsafe condition is detected, the safety manager of the control system triggers a warning or activates a predetermined interlock to prevent unsafe operation of the RV and its deployable components.

A hybrid deterministic override to cloud based probabilistic advanced driver assistance systems. Under default driving conditions, an ego vehicle is controlled by a probabilistic controller in a cloud. An overall gap between the ego vehicle and a leading vehicle is divided into an emergency collision gap and a driver specified gap. The vehicle sensors monitor the overall gap. When the gap between the ego vehicle and the leading vehicle is less than or equal to the emergency collision gap, a deterministic controller of the ego vehicle overrides the cloud based probabilistic controller to control the braking and acceleration of the ego vehicle.

A vehicle control system uses reliability of an input signal of an autonomous vehicle to safely travel through an intersection or a crossroad. The system includes a first calculating unit that calculates reliability for behavior information of a front vehicle and a second calculating unit calculates reliability for state information of a traffic light in the crossroad or the intersection based on a surrounding vehicle. A third calculating unit calculates reliability for brake light information of the front vehicle and a fourth calculating unit calculates reliability for flow information of the surrounding vehicle passing the crossroad or the intersection. A determining unit generates a vehicle control signal according to the calculated reliability.