Provided are a system and a method for testing an ability of an automated vehicle to pass a traffic circle without traffic lights. The system includes an automated vehicle, a traffic circle, a control center, an attitude sensor and a panorama camera. In the method, the control center receives a test request and then sends a driving command to drive the automated vehicle to enter the traffic circle to start the test. The attitude sensor obtains a tilt angle and sends it to the control center in real time. The panorama camera is configured to obtain a driving trajectory, a driving speed and a state of turn signals of the automated vehicle. The obtained information is compared to a standard by the control center to evaluate the ability of the automated vehicle to pass the traffic circle without traffic lights.

A vehicle collision determination system includes an acceleration sensor fixedly positioned to a front end of the vehicle and configured to measure acceleration at a fixed position, a detector configured to detect whether the vehicle collides using the acceleration measured by the acceleration sensor, a metrics setting unit configured to set at least one metric using the acceleration measured by the acceleration sensor when the detector detects collision of the vehicle, and a determiner configured to determine whether an object that has collided with the vehicle is a pedestrian using the at least one metric set by the metrics setting unit.

According to various embodiments, systems and methods are provided for use by an ADV to nudge incoming objects in a self-reverse lane. In an embodiment, an ADV determines that a self-reverse lane has a predetermined width before entering the self-reverse lane. After entering the self-reverse lane, the ADV can follow a first reference line at the center of the self-reverse lane. In response to detecting an incoming object, the ADV creates an alternative lane in the self-reverse lane by temporarily modifying a high definition map. The ADV subsequently follows a second reference line in the alternative lane to nudge the incoming object. In response to detecting that the incoming object has passed and the self-reverse lane is clear, the ADV can drive back to the center of the self-reverse lane, to continue to follow the first reference line in the self-reverse lane.

A method to reduce a vehicle-hazard potential, the method including the steps of: detecting, via a sensor, one or more objects in a vehicle environment; determining whether these objects are of a specific type; and based on the specific type of the one or more objects, deterring the one or more objects from colliding with a vehicle.

According to one aspect, an overall airbag system of an autonomous vehicle uses a combination of sensors to deploy one or more external airbags in advance of a collision or as a collision occurs, for the purpose of protecting vulnerable persons. By using sensor data, combined with perception and prediction algorithms, an autonomous driving system may deploy a substantially optimal combination of external airbags for a given the size of a vulnerable person, vehicle speed, and/or collision timing. In addition to cushioning impact during a collision, the deployment of multiple external airbags may also control kinematics of vulnerable persons, as for example by addressing brain injuries in pedestrians due to rotational kinematics.

A control system of a vehicle, the vehicle including a detection unit for detecting external information related to an outside of surroundings of the vehicle, the external information being used to control a driven state of the vehicle is provided. The control system performs a method comprising: obtaining map information of surroundings of a route on which the vehicle travels based on position information of the vehicle, and specifying, from among pieces of detection range information corresponding to the map information, detection range information corresponding to the detection unit; and controlling the driven state of the vehicle based on the specified detection range information and the external information.

The disclosure includes embodiments for pedestrian navigation by a group of connected vehicles executing a collaborative computing process. In some embodiments, a method includes analyzing pedestrian data generated by a pedestrian device and sensor data generated by the group of connected vehicles to determine digital twin data from a set that correlates with a scenario described by the pedestrian data and the sensor data. The digital twin data is an output of a historical digital twin simulation. The method includes predicting, based on the digital twin data, that the pedestrian is at risk of a collision. The method includes determining modified path data describing a modified walking path for the pedestrian. The method includes transmitting the modified path data to the pedestrian device so that the pedestrian is informed about the modified walking path and the risk is modified.

A driving assist system assists driving of a vehicle. A deceleration target includes at least one of a preceding vehicle, a mandatory stop line, a mandatory stop sign, a traffic signal, and a stop line before the traffic signal that exist ahead of the vehicle. A risk factor includes at least one of a pedestrian, a bicycle, a motorcycle, an oncoming vehicle, and a parked vehicle that exist ahead of the vehicle. The driving assist system executes: deceleration assist control that automatically decelerates the vehicle before the deceleration target; and risk avoidance control that automatically performs at least one of steering and deceleration of the vehicle so as to avoid the risk factor. When both the deceleration assist control and the risk avoidance control operate concurrently, the driving assist system notifies a driver of the vehicle of not the deceleration target but the risk factor.

A driving assist system assists driving of a vehicle. A deceleration target includes at least one of a preceding vehicle, a mandatory stop line, a mandatory stop sign, a traffic signal, and a stop line before the traffic signal that exist ahead of the vehicle. A risk factor includes at least one of a pedestrian, a bicycle, a motorcycle, an oncoming vehicle, and a parked vehicle that exist ahead of the vehicle. The driving assist system executes: deceleration assist control that automatically decelerates the vehicle before the deceleration target; and risk avoidance control that automatically performs at least one of steering and deceleration of the vehicle so as to avoid the risk factor. When both the deceleration assist control and the risk avoidance control operate concurrently, the driving assist system notifies a driver of the vehicle of not the risk factor but the deceleration target.

An apparatus for controlling a lane change of an autonomous vehicle and a method thereof. The apparatus includes a learning device that performs deep learning by subdividing situation information into groups to be considered when the autonomous vehicle changes a lane, and a controller that controls the lane change of the autonomous vehicle based on a learning result of the learning device.