A driving assistance apparatus is configured to perform an assistance control of assisting in driving a vehicle, when a first condition and a second condition are satisfied, in a situation in which a target is recognized. The driving assistance apparatus is provided with: a determinator configured to determine a state of the assistance control. The determinator is configured (i) to determine that the state of the assistance control is a standby state if a standby condition is satisfied, wherein the standby condition requires that the first condition is satisfied, but the second condition is not satisfied, in the situation in which the target is recognized, and (ii) to determine that the state of the assistance control is an interruption state if an interruption condition is satisfied, wherein the interruption condition requires that the first condition is no longer satisfied while the satisfaction of the standby condition is continued.

A vehicle sound effect generation apparatus includes a running state detecting unit that detects a running state of a vehicle; a lateral input amount setting unit that sets, based on the running state detected by the running state detecting unit, a lateral input amount in which a physical amount relating to at least one of a movement of the vehicle in a width direction and a movement of the vehicle in a turning direction is a parameter; an adjustment wave sound selector that selects one or more integer-order adjustment wave sounds having an integer-order frequency component, based on the lateral input amount; and a sound effect generation unit that synthesizes a fundamental wave sound having a fundamental frequency component with the one or more integer-order adjustment wave sounds selected.

In one embodiment, a data processing system for an autonomous driving vehicle (ADV) includes a processor, and a memory coupled to the processor to store instructions, which when executed by the processor, cause the processor to perform operations. The operations include generating a station-time (ST) graph based on perception data obtained from one or more sensors of the ADV, the ST graph including representing a location of an obstacle at different points in time, obtaining a tensor based on the ST graph, the tensor including a plurality of layers, the plurality of layers including a first layer having data representing one or more obstacles on a path in which the ADV is moving, applying a machine-learning model to the plurality of layers of the tensor to generate a plurality of numerical values, the plurality of numerical values defining a potential path trajectory of the ADV, and determining a path trajectory of the ADV based on the plurality of numerical values.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

A system and method includes an autonomous agent having a communication interface that enables the autonomous agent to communicate with a plurality of infrastructure sensing devices; a plurality of distinct health monitors that monitor distinct operational aspects of the autonomous agent; an autonomous state machine that computes a plurality of allowed operating states of the autonomous agent based on inputs from the plurality of distinct health monitors; a plurality of distinct autonomous controllers that generate a plurality of distinct autonomous control instructions; and an arbiter of autonomous control instructions that: collects, as a first input, the plurality of autonomous control instructions generated by each of the plurality of distinct autonomous controllers; collects, as a second input, data relating to the plurality of allowed operating state of the autonomous agent; and selectively enables only a subset of the autonomous control instructions to pass to driving components of the autonomous agent.

An information processing device to be mounted on a vehicle includes a processor. The processor is configured to determine, in response to a request for the vehicle, one mode out of a plurality of modes defining behavior of the vehicle, make transition of a status of the vehicle among a plurality of statuses that is based on a state and a sub-mode, and control the vehicle based on the status of the vehicle that has been achieved by the transition. The state and the sub-mode are permitted in the determined mode.

A system for a host vehicle and includes a memory and a vehicle control module. The memory stores autonomous status bits for each local vehicle. The autonomous status bits indicate whether the corresponding local vehicle is operating in a non-autonomous, semi-autonomous, or fully autonomous mode. The vehicle control module: generates autonomous status bits indicative of an autonomous status level of the host vehicle and transmits the autonomous status bits in a message to first vehicle communication devices; determines that local vehicles are in a local environment of the host vehicle; receives messages including the autonomous status bits of the local vehicles from second vehicle communication devices; and unless operating in a fully autonomous mode, generates a request for a driver of the host vehicle to take control of the host vehicle based on the autonomous status bits of the host vehicle and the local vehicles.

The present invention concerns a driving assistance method and system (100) for a road vehicle (1). The driving assistance system (100) comprises a sensor set (101), a data storage device (102) and an an output device (104,105). The sensor set (101) detects, within a traffic scene including the road vehicle (1), a set of road users and, for each road user of said set of road users, a current state including a current speed and a current position. The data storage device (102) comprises a finite plurality of behavioral models. The data processor (103), which is connected to the sensor set (101) and to the data storage device (102), assigns a behavioral model, from among the finite plurality of behavioral models, to each road user, probabilistically estimates, for each road user, a belief state comprising a set of alternative subsequent states and corresponding probabilities, each alternative subsequent state including a speed and a position, according to the behavioral model assigned to each road user, and determines a risk of collision of the road vehicle (1) with a road user, based on the probabilistically estimated future state of each road user. The output device (104,105) is connected to the data processor (103) and outputs a driver warning signal and/or executing an avoidance action if the risk of collision exceeds a predetermined threshold