A driver assistance system that notifies the driver of the braking information of the preceding vehicle or improves the sensitivity of the emergency braking system if the brake lamp of the preceding vehicle is failed includes: a first sensor mounted in a vehicle, having a front field of view of the vehicle, and configured to acquire a front image data; a second sensor selected from a group consisting of a radar sensor and a lidar sensor, mounted in the vehicle, having a front field of sensing of the vehicle, and configured to acquire a front detection data; and a controller including a processor configured to process the front image data and the front detection data, and the controller is configured to: detect a velocity of the preceding vehicle travelling in front of the vehicle and a brake lamp of the preceding vehicle in response to processing the front image data and the front detection data; and determine whether the brake lamp of the preceding vehicle is failed based on the velocity of the preceding vehicle and a lighting of the brake lamp of the preceding vehicle.

An integrated chassis control system includes a first sensor configured to sense a first vehicle driving in a lane adjacent to a lane in which an own vehicle is driving and to sense behavior information of the first vehicle, a second sensor configured to sense a variation in behavior of the own vehicle, a first determinator configured to determine a degree of influence of a side wind, which is predicted to occur due to the first vehicle, based on the behavior information of the first vehicle, a second determinator configured to determine a variance in abnormal behavior of the own vehicle based on information sensed by the second sensor, a first controller configured to perform a semi-active chassis system control, and a second controller configured to perform an active chassis system control.

In one embodiment, a vehicle merge control system generates sensor data that indicate a detected vehicle in an environment of the ego vehicle, identifies a conflict based at least in part on the sensor data indicating that the detected vehicle and the ego vehicle are traveling: 1) in adjacent lanes that will merge into a single lane, and 2) at respective speeds that will result in the detected vehicle entering within a threshold range of the ego vehicle, determines, upon identification of the conflict, merge position assignments for the ego vehicle and the detected vehicle based at least in part on a game theory cost function applied to game theory actions that result in the merge position assignments exclusively being either a lead vehicle or a follower vehicle, and outputs an acceleration rate to achieve the merge position assignment for the ego vehicle.

In an embodiment, a method includes: receiving ambient sound; determining if the ambient sound includes a siren; in accordance with determining that the ambient sound includes a siren, determining a first location associated with the siren; receiving a camera image; determining if the camera image includes a flashing light; in accordance with determining that the camera image includes a flashing light, determining a second location associated with the flashing light; 3D data; determining if the 3D data includes an object; in accordance with determining that the 3D data includes an object, determining a third location associated with the object; determining a presence of an emergency vehicle based on the siren, detected flashing light and detected object; determining an estimated location of the emergency vehicle based on the first, second and third locations; and initiating an action related to the vehicle based on the determined presence and location.

A vehicle control system and method includes obtaining a size of a vehicle system, identifying locations of different portions of the vehicle system, and determining one or more of a) whether the vehicle system is disposed within or across an intersection of routes or b) a predicted time of arrival at which the vehicle system will be disposed within or across the intersection based on the size of the vehicle system and the locations of the different portions of the vehicle system.

In one embodiment, an exemplary method includes receiving, at a simulation platform, a record file recorded by a manually-driving ADV on a road segment, the simulation platform including a first encoder, a second encoder, and a performance evaluator; simulating automatic driving operations of a dynamic model of the ADV on the road segment based on the record file, the dynamic model including an autonomous driving module to be evaluated. The method further includes: for each trajectory generated by the autonomous driving module during the simulation: extracting a corresponding trajectory associated with the manually-driving ADV from the record file, encoding the trajectory into a first semantic map and the corresponding trajectory into a second semantic map, and generating a similarity score based on the first semantic map and the second semantic map. The method also includes generating an overall performance score based on each similarity score.

Systems and methods for operating an autonomous vehicle. The methods comprising: obtaining, by a computing device, a LiDAR dataset; plotting, by a computing device, the LiDAR dataset on a 3D graph to define a 3D point cloud; using, by a computing device, the LiDAR dataset and contents of a vector map to define a cuboid on the 3D graph that encompasses points of the 3D point cloud that are associated with an object in proximity to the vehicle, where the vector map comprises lane information; and using the cuboid to facilitate driving-related operations of the autonomous vehicle.

The driving assistance apparatus comprises a camera device configured to obtain a camera image by taking a picture of a predetermined area ahead of a host vehicle; and a control unit configured to drive the host vehicle so as to cause an acceleration of the host vehicle to become equal to a follow-up acceleration to make an inter vehicle distance to a preceding vehicle coincide with a target inter vehicle distance. When the preceding vehicle is a large-size vehicle, the control unit obtains the target inter vehicle distance for causing a traffic-signal-equipment-distance indicative of a distance to a traffic signal equipment of when the traffic signal equipment is detected above the preceding vehicle in the camera image to be longer than a stop-required-distance for which the host vehicle travels until the host vehicle stops under the assumption that the host vehicle decelerates at a predetermined deceleration.

A system for determining a content of a message used to coordinate interactions among vehicles can include a processing device and a memory. The memory can store a discretization module and a communications module. The discretization module can include instructions that when executed by the processing device cause the processing device to: (1) analyze a critical maneuver trajectory of an ego vehicle and a current trajectory of another vehicle to determine an existence of a critical maneuver situation and (2) produce a discretized representation of a portion of the critical maneuver trajectory of the ego vehicle. A form of the discretized representation can be based on the existence of the critical maneuver situation. The communications module can include instructions that when executed by the processing device cause the processing device to communicate the discretized representation as the content of the message used to coordinate interactions among vehicles.

Systems and methods are provided for intelligent driving monitoring systems, advanced driver assistance systems and autonomous driving systems, and providing alerts to the driver of a vehicle, based on anomalies detected between driver behavior and environment captured by the outward facing camera. Various aspects of the driver, which may include his direction of sight, point of focus, posture, gaze, is determined by image processing of the upper visible body of the driver, by a driver facing camera in the vehicle. Other aspects of environment around the vehicle captured by the multitude of cameras in the vehicle are used to correlate driver behavior and actions with what is happening outside to detect and warn on anomalies, prevent accidents, provide feedback to the driver, and in general provide a safer driver experience.