This application discloses a method and an apparatus for detecting a complexity of a traveling scenario of a vehicle, comprising: obtaining a travelling speed of the vehicle and a travelling speed of a target vehicle; determining, based on the traveling speed of the vehicle and the traveling speed of the target vehicle, a dynamic complexity of a traveling scenario in which the vehicle is located; determining static information of each static factor in the traveling scenario in which the vehicle is currently located; obtaining, based on the static information of each static factor, a static complexity of the traveling scenario in which the vehicle is located; and obtaining, based on the dynamic complexity and the static complexity, a comprehensive complexity of the traveling scenario in which the vehicle is located.

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).

A system, method, and computer-readable medium having instructions stored thereon to enable an ego vehicle having an autonomous driving function to estimate and traverse a curved segment of highway utilizing radar sensor data. The radar sensor data may comprise stationary reflections and moving reflections. The ego vehicle may utilize other data, such as global positioning system data, for the estimation and traversal. The estimation of the curvature may be refined based upon a lookup table or a deep neural network.

This application provides a data processing method performed a computer device. The method includes: generating an initial predicted lane change acceleration corresponding to a target vehicle in a current lane; generating target predicted position information corresponding to the target vehicle according to a predicted lane change time duration taken for the target vehicle to change from the current lane to a target lane, the target lane being a lane to which the target vehicle is expected to change to; determining a target obstacle vehicle in the target lane and adjacent to the target vehicle according to the target predicted position information; determining, according to a predicted position relationship between the target obstacle vehicle and the target vehicle, a target predicted lane change acceleration; and controlling, according to the target predicted lane change acceleration, the target vehicle to change from the current lane to the target lane.

A vehicle includes: an input configured to receive a destination; a display; a driver assistance system configured to control a behavior of the vehicle based on surrounding environment information; and a controller configured to control the display to display a driving route. The controller may be configured to determine, when a distance between a branch point on the driving route and the vehicle reaches a first distance, a possibility that the vehicle deviates from the driving route based on GPS information, vehicle speed information, and the surrounding environment information, and search for, when the possibility is greater than or equal to a preset threshold, a deviation route for reaching the destination based on the deviated direction and control the display to display the deviation route until the distance between the branch point on the driving route and the vehicle reaches a second distance.

A method for setting a tuning parameter for an Automated Driving System (ADS) of a vehicle is disclosed. A corresponding non-transitory computer-readable storage medium, vehicle control device and a vehicle comprising such a control device are also disclosed. The method comprises receiving environmental data from a perception system of the vehicle, said environmental data comprising a plurality of environmental parameters, determining, by means of a self-learning model, an environmental scenario based on the received environmental data; setting the tuning parameter for the ADS based on the self-learning model and the determined environmental scenario, the tuning parameter defining a dynamic parameter of the ADS, receiving at least one signal representative of a vehicle user feedback on the set tuning parameter, and updating the self-learning model for the set tuning parameter for the identified environmental scenario based on the received vehicle user feedback.

A vehicle may transmit, to a network entity, a lane use request message associated with a lane for the vehicle. The network entity may identify lane information associated with a lane for a vehicle. The network entity may identify vehicle information associated with the vehicle. The network entity may transmit, to the vehicle, and the vehicle may receive, from the network entity, a lane use grant message based on at least one of the identified lane information or the identified vehicle information. The lane use grant message may be indicative of a permission for the vehicle to use the lane. The vehicle may not be permitted to use the lane without the permission. The lane may correspond to a flexible direction lane, an emergency lane, a road shoulder, an HOV lane, or a passing lane.

Implementations for an intelligent road barrier (IRB) system can include a plurality of road barriers located along a roadway, and a plurality of IRB kits, each IRB kit being including a set of sensors configured to obtain sensor data representative of at least a portion of a road state of the roadway proximate to the respective road barrier, a data processing unit configured to process at least a portion of the sensor data, a communication unit configured to send at least a portion of the sensor data and to receive sensor data from the one or more other IRB kits, and a state conveyance system configured to convey at least a portion of road state data to one or more vehicles associated with the roadway, the at least a portion of road state data being determined based on the at least a portion of the sensor data.

A method is disclosed for analyzing historical accident information to adjust driving actions of an autonomous vehicle over a travel route in order to avoid accidents which have occurred over the travel route. Historical accident information for the travel route can be analyzed to, for example, determine accident types which occurred over the travel route and determine causes and/or probable causes of the accident types. In response to determining accident types and causes/probable causes of the accident types over the travel route, adjustments can be made to the driving actions planned for the autonomous vehicle over the travel route. In addition, in an embodiment, historical accident information can be used to analyze available travel routes and select a route which presents less risk of accident than others.

Path generation apparatus configured to generate target path of own vehicle traveling in own lane, includes: sensor configured to detect objects in surrounding area of own vehicle; and electronic control unit including processor and memory. Electronic control unit is configured to perform: recognizing adjacent vehicle traveling in adjacent lane adjacent to own lane from among detected objects; determining whether own lane is congested based on travel speed of own vehicle and determining whether adjacent lane is congested based on recognition result of adjacent lane; and generating target path of own vehicle. Generating target path includes generating target path on side away from adjacent lane with respect to that of case where own lane is determined to be congested and adjacent lane is determined to be congested, in case where own lane is determined to be congested and adjacent lane is determined not to be congested.