A method for predicting a trajectory (108) of a vehicle (102) uses first data captured by a first sensor of a first vehicle (101) to determine a first position, a first acceleration, a first velocity and a first yaw rate of a second vehicle (102) and uses second data captured by a second sensor of the first vehicle (101) to determine a second position, a second acceleration, a second velocity and a second yaw rate of the second vehicle (102). The method uses these first and second sets of information with a vehicle model to determine first and second lists of points for predicting the trajectory. One or more parameters of a model for the prediction of the trajectory (108) are determined depending on the first and second lists of points, and the prediction of the trajectory (108) is determined depending on the model defined by these parameters.

A behavior prediction apparatus specifies a first object that affects a behavior of a vehicle from objects present around the vehicle. The behavior prediction apparatus performs a prediction process of extracting a second object that affects a behavior of the first object among a plurality of objects present around the first object and predicting a behavior. The behavior prediction apparatus sets the extracted second object as a new first object, and performs a prediction process of extracting a new second object affecting the behavior of the new first object and predicting the behavior. The behavior prediction apparatus repeats the prediction process by a predetermined number of times. The behavior prediction apparatus predicts the behavior of the first object in the first prediction process based on the behavior of each of the second objects subjected to the prediction process.

A vehicle includes a driving device configured to control a speed of the vehicle, a camera configured to detect a surrounding vehicle, and a controller configured to determine the speed of the vehicle. The controller also calculates an image vector variation amount of the surrounding vehicle when the speed of the vehicle is lower than a predetermined speed and calculates a safety distance between the vehicle and a preceding vehicle based on the image vector variation amount of the surrounding vehicle when the image vector variation amount of the surrounding vehicle satisfies a predetermined condition. The controller also controls the driving device to control the speed of the vehicle depending on the calculated safety distance.

A security system is provided for an autonomous vehicle driving on a road. The autonomous vehicle has a sensor unit for collecting sensor information, and a planning unit for generating planning data for the autonomous vehicle. The security system is characterized by a reception module, configured to receive the sensor information and the planning data; a detection module, configured to detect, based on the sensor information, a driving situation of the autonomous vehicle in which one or more objects on the road cause the autonomous vehicle to slow down; an assessment module, configured to assess the driving situation based on the detection of the driving situation, the sensor information, and the planning data, as a potential plunder incident; and a reaction module, configured to execute, in case of the potential plunder incident, an emergency reaction of the autonomous vehicle.

A control apparatus for controlling a vehicle includes a driving motor configured to drive the vehicle by outputting motor torque based on a supply voltage from a battery, and an engine configured to drive the vehicle by outputting engine torque. The control apparatus may acquire driving mode data which is calculated based on traffic information from the current position to the destination of the vehicle and dimension information of the vehicle, and control the vehicle to drive to the destination according to a driving mode which is determined by applying a travelling condition of the vehicle to the acquired driving mode data, where the power distribution ratio of the motor torque to the engine torque is reflected in the driving mode data.

A traffic control device of the present disclosure includes: a communication unit which receives target passing direction information and traffic information about moving objects in an intersection area transmitted from a traffic environment recognition device which acquires the traffic information; a pass schedule generation unit which predicts behaviors in the intersection area for each moving object to pass an intersection, on the basis of the traffic information and the target passing direction information, and generates a pass schedule in the intersection for each moving object; a collision judgment unit which judges a collision occurrence possibility in the intersection on the basis of the pass schedules; a passing order rank setting unit which sets passing order ranks if it is judged that collision will occur; and an adjusted pass schedule generation unit which generates adjusted pass schedules.

Providing user assistance in a vehicle includes identifying a traffic behavior of an object in an environment surrounding the vehicle based on an evaluation of information about the environment surrounding the vehicle while the vehicle is in the midst of manual operation, and issuing an alert to a user prompting the user to implement defensive manual operation. The user assistance further includes receiving a traffic behavior model that describes a predominating traffic behavior of a like population of reference objects, and issuing the alert to a user prompting the user to implement defensive manual operation in response to identifying that the traffic behavior of the object does not match the predominating traffic behavior of the like population of reference objects. Under the defensive manual operation, the traffic behavior of the object is addressed.

While operating a host vehicle in a first lane on a road, respective first statistical probabilities of a) an average speed of a plurality of vehicles, including the host vehicle, operating in the first lane, b) an average distance between the vehicles operating in the first lane, and c) an average number of braking events performed by the vehicles operating in the first lane are determined based on sensor data. A high traffic density probability for the road is determined based on the first statistical probabilities. A host vehicle assist feature is transitioned between an enabled state and a disabled state based on the high traffic density probability for the road being greater than a threshold probability.

A system uses a machine learning based model to determine attributes describing states of mind and behavior of traffic entities in video frames captured by an autonomous vehicle. The system classifies video frames according to traffic scenarios depicted, where each scenario is associated with a filter based on vehicle attributes, traffic attributes, and road attributes. The system identifies a set of video frames associated with ground truth scenarios for validating the accuracy of the machine learning based model and predicts attributes of traffic entities in the video frames. The system analyzes video frames captured after the set of video frames to determine actual attributes of the traffic entities. Based on a comparison of the predicted attributes and actual attributes, the system determines a likelihood of the machine learning based model making accurate predictions and uses the likelihood to generate a navigation action table for controlling the autonomous vehicle.

Operating a subject vehicle equipped with an adaptive cruise control system includes setting initial states for control parameters, including setting a desired vehicle speed and determining a desired following gap range, wherein the desired following gap range is associated with a lead vehicle. Operation is controlled via the adaptive cruise control system based upon the initial states for the control parameters. Operation also includes monitoring for presence of the lead vehicle. Upon detecting presence of the lead vehicle, an actual following gap is determined between the subject vehicle and the lead vehicle, and the initial states of the control parameters associated with the adaptive cruise control system are adjusted based upon the actual following gap between the subject vehicle and the lead vehicle, and the desired following gap range. Operation is controlled via the adaptive cruise control system based upon the adjusted initial states of the control parameters.