In one embodiment, a method, apparatus, and system for automatically switching on an emergency light at an autonomous driving vehicle (ADV) is disclosed. A present speed of an ADV at a first time instant is determined. A present deceleration of the ADV at the first time instant is determined. Whether the present speed satisfies a present speed condition and whether the present deceleration satisfies a present deceleration condition at the first time instant are determined. In response to determining that the present speed satisfies the present speed condition and that the present deceleration satisfies the present deceleration condition, whether a recent deceleration history of the ADV satisfies a recent deceleration history condition and whether an expected deceleration of the ADV satisfies an expected deceleration condition are determined. If either condition is satisfied, an emergency light of the ADV is automatically switched on.

A method of underwater tsunami detection includes detecting a trigger event using disruption of at least one of a plurality of hard disk drives (HDDs), each different one of the plurality of HDDs in a different one of a plurality of autonomous underwater vehicles (AUVs). A time and location of each of the at least one HDD for the trigger event is logged. Based on at least one of the HDD disruptions, times, and locations of the at least one HDD of the plurality of HDDs, a size, strength, and direction of a tsunami caused by the trigger event is determined. Information regarding the tsunami is transmitted to a monitoring station.

An automatic driving vehicle includes a touch panel that displays various buttons. An operator can input a drive control instruction in relation to the automatic driving vehicle, with the buttons displayed on the touch panel. In addition to the touch panel, the automatic driving vehicle includes a mechanical door switch for opening and closing an automatic door and a mechanical ramp switch for extending and retracting an automatic ramp, both being disposed spaced apart from the touch panel.

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.

Current data for a geographic area is accessed. The current data comprises at least one of (a) current traffic data for the geographic area, (b) current incident data for the geographic area, or (c) current weather data for the geographic area. Based on the current data, autonomous driving instructions are determined for the geographic area. A notification comprising the autonomous driving instructions is provided such that the notification is received by a vehicle apparatus located within the geographic area or expected to enter the geographic area based on a route being traversed by a vehicle corresponding to the vehicle apparatus. The vehicle apparatus is onboard the vehicle and is configured to control the vehicle in accordance with the autonomous driving instructions.

A robot includes an operation control unit that selects a motion of the robot, a drive mechanism that executes a motion selected by the operation control unit, and a remaining battery charge monitoring unit that monitors a remaining charge of a rechargeable battery. Behavioral characteristics of the robot change in accordance with the remaining battery charge. For example, a motion with a small processing load is selected at a probability that is higher the smaller the remaining battery charge. Referring to consumption plan data that define a power consumption pace of the rechargeable battery, the behavioral characteristics of the robot may be caused to change in accordance with a difference between the remaining battery charge scheduled in the consumption plan data and the actual remaining battery charge.

A vehicle control apparatus comprises a first detection unit configured to have a first detection range, a second detection unit configured to have a second detection range which at least partially overlaps the first detection range, and a vehicle control unit configured to be capable of performing vehicle control based on a first control state and vehicle control based on a second control state which has a high vehicle control automation rate or a reduced degree of vehicle operation participation requested to a driver compared to the first control state. The vehicle control unit performs control to shift from the first control state to the second control state based on a condition that a match degree between pieces of preceding object information of a vehicle detected by the first detection unit and the second detection unit.

Systems and methods to authenticate a vehicle operator for an autonomous vehicle on a vehicle service platform are provided. In one example embodiment, a computer-implemented method includes obtaining authentication request data indicative of an authentication request, the authentication request data including at least an operator identifier associated with the vehicle operator and a vehicle identifier associated with the autonomous vehicle. The method includes providing a service code associated with the authentication request to the autonomous vehicle. The method includes obtaining from a user device in response to providing the service code to the autonomous vehicle, operator data associated with the authentication request, the operator data including the service code. The method includes determining an authentication result associated with the authentication request based at least in part on the service code and the operator data. The method includes providing the authentication result to the user device.

Systems, apparatuses and methods may provide for technology that generates a sequence of predictive maps based on a sequence of historical maps and overlays the sequence of predictive maps on one another to obtain a map overlay. The technology may also apply an attenuation factor to the map overlay. In one example, the map overlay includes a grid of cells and each cell includes an occupation probability in accordance with the attenuation factor.

An autonomous driving apparatus for accompanied driving in an environment that includes a companion and an obstacle includes a sensor, processing circuitry, and a driver. The sensor may generate sensor data. The processing circuitry may define a current state of the autonomous driving apparatus based on processing the sensor data to determine respective positions of the companion and the obstacle in the environment and select a first tracking point of a plurality of tracking points at least partially surrounding the position of the companion in the environment based on the current state, a position of each tracking point of the plurality of tracking points in the environment defined by the position of the companion in the environment. The driving apparatus drive mechanism may move the autonomous driving apparatus to the first tracking point to cause the autonomous driving apparatus to accompany the companion in the environment.