Disclosed are an apparatus and method for preventing a vehicle collision. The apparatus includes a communication unit wirelessly communicating with a surrounding vehicle, a sensing unit detecting the surrounding vehicle and a lane, a traveling control module configured to control steering or braking of an ego vehicle, and a controller configured to determine whether the ego vehicle can perform steering avoidance driving toward a neighboring lane, through the sensing unit, based on information on a driving mode of a vehicle ahead received through the communication unit, if it is checked through the communication unit that a vehicle ahead of the vehicle ahead is in a stop state and to control the ego vehicle to perform the steering avoidance driving toward the neighboring lane or to urgently brake through the traveling control module, based on a result of the determination.

A vehicle control method includes recognizing a periphery of a vehicle, a first vehicle and a second vehicle present in the periphery, setting a target area between the first vehicle and the second vehicle based on a reference position of a first type of the first vehicle present in a lane of a lane change destination and a reference position of the first type of the second vehicle behind the first vehicle and present in the lane, and controlling the vehicle to enter the target area set based on a reference position of a second type of the first vehicle and a reference position of the second type of the second vehicle.

A method for selecting a content forwarding node in a vehicle ad-hoc network on the basis of service type, comprising the following steps: calculating a bandwidth occupation proportion factor according to the type of requested content information and a bandwidth occupation situation of a neighboring vehicle which can perform forwarding in a one-hop transmission range; defining a virtual arrival time of a candidate forwarding vehicle to an initial request vehicle according to the distance between the neighboring vehicle and the initial request vehicle and a driving speed of the neighboring vehicle; calculating a forwarding node impact factor according to the bandwidth occupation proportion factor and the virtual arrival time; and selecting a forwarding node according to the forwarding node impact factor.

A method for controlling a vehicle based on a prediction from a semantic map is presented. The method includes receiving a snapshot of an environment from one or more sensors. The method also includes generating the semantic map based on the snapshot and predicting an action of a dynamic object in the snapshot based on one or more surrounding objects. The method still further includes controlling an action of the vehicle based on the predicted action.

A method for three-dimensional modeling. The method may include: acquiring coordinate points of obstacles in a surrounding environment of an autonomous driving vehicle in a vehicle coordinate system; determining a position of eyes of a passenger in the autonomous driving vehicle, and establishing an eye coordinate system using the position of the eyes as a coordinate origin; converting the coordinate points of the obstacles in the vehicle coordinate system to coordinate points in the eye coordinate system, and determining a visualization distance between the obstacles in the surrounding environment based on an observation angle of the eyes; and performing three-dimensional modeling of the surrounding environment, based on visualization distance between the coordinate points of the obstacles in the eye coordinate system and the obstacles.

Embodiments of this application provide a method and an apparatus for determining drivable region information. The method includes obtaining first information, where the first information includes information about an initial drivable region determined based on at least one image, and the at least one image is from at least one camera module. The method also includes obtaining second information, where the second information includes radar detection information. The method further includes determining first drivable region information based on the first information and the second information.

A system and method for implementing reward based strategies for promoting exploration that include receiving data associated with an agent environment of an ego agent and a target agent and receiving data associated with a dynamic operation of the ego agent and the target agent within the agent environment. The system and method also include implementing a reward function that is associated with exploration of at least one agent state within the agent environment. The system and method further include training a neural network with a novel unexplored agent state.

Disclosed are an apparatus and method for generating a link for each lane when there is a plurality of exit lanes. The apparatus may acquire information about a speed of a probe vehicle from the probe vehicle, and determine a section for generating a divided link along a lane, based on a speed difference of the probe vehicle in an exit direction. Accordingly, it is possible to improve the accuracy and reliability of the speed of each lane.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for determining the likelihood that a particular event would occur during a navigation interaction using simulations generated by sampling from agent data. In one aspect, a method comprises: identifying an instance of a navigation interaction that includes an autonomous vehicle and agents navigating in an environment; generating multiple simulated interactions corresponding to the instance, comprising, for each simulated interaction: identifying one or more agents; for each identified agent and for each property that characterizes behavior of the identified agent, obtaining a probability distribution for the property; sampling a respective value from each of the probability distributions; and simulating the navigation interaction in accordance with the sampled values; and determining a likelihood that the particular event would occur during the navigation interaction based on whether the particular event occurred during each of the simulated interactions.

Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: (i) acquiring a predicted object trajectory for an object, (ii) acquiring a set of anchor points along the lane for the SDC, (iii) for each one of the set of anchor points, determining a series of future moments in time when the SDC is potentially located at the respective one of the set of anchor points, thereby generating a matrix structure including future position-time pairs, (iv) for each future position-time pair in the matrix structure, using the predicted object trajectory for determining a distance between a closest object to the SDC as if the SDC is located at the respective future position-time pair, and (v) storing the distance between the closest object to the SDC in association with the respective future position-time pair in the matrix structure.