A control device associated with an autonomous vehicle detects that an emergency personnel is altering traffic on a road using an emergency-related hand signal to divert the traffic from a road anomaly, such as a road accident. The control device determines an interpretation of the emergency-related hand signal. The control device determines a proposed trajectory for the autonomous vehicle according to the interpretation of the emergency-related hand signal. In certain embodiments, the control device may navigate the autonomous vehicle according to the interpretation of the emergency-related hand signal. In certain embodiments, the control device may transmit the proposed trajectory to an oversight server for confirmation. In certain embodiments, the oversight may confirm or override the proposed trajectory.

An apparatus comprises a plurality of sensors, a digital map. and a control unit. The plurality of sensors may be configured to detect information about an exterior environment of a vehicle. The digital map may be configured to provide information about roadways in a vicinity of the vehicle. The control unit (i) may comprise an interface configured to receive (a) sensor status signals, (b) sensor-based information, and (c) map-based information, and (ii) may be configured to (a) determine whether an operational situation exists that is unsafe for an advanced driver-assistance systems (ADAS) automation feature to be activated or remain active based on the sensor-based information, the map-based information, and the sensor status signals, and (b) generate an activation control signal to restrict activation of the ADAS automation feature when an unsafe operational situation exists.

Systems and methods for lane marking and road sign recognition are provided. The system aligns image level features between a source domain and a target domain based on an adversarial learning process while training a domain discriminator. The target domain includes one or more road scenes having lane markings and road signs. The system selects, using the domain discriminator, unlabeled samples from the target domain that are far away from existing annotated samples from the target domain. The system selects, based on a prediction score of each of the unlabeled samples, samples with lower prediction scores. The system annotates the samples with the lower prediction scores.

A driving support apparatus according to the invention estimates the position of a moving body by controlling a position estimation unit when the tracking-target moving body leaves a first area or a second area to enter a blind spot area and detects the position of the moving body by controlling a position detection unit when the moving body leaves the blind spot area to enter the first area or the second area. In this manner, the trajectory of the tracking-target moving body is calculated so that the trajectory of the moving body detected in the first area or the second area and the trajectory of the moving body estimated in the blind spot area are continuous to each other and driving support is executed based on the calculated trajectory of the tracking-target moving body.

A drive assist apparatus according to the present disclosure includes: a dynamic information acquisition unit acquiring, as dynamic information, at least one of subject vehicle dynamic information, the other vehicle dynamic information, and obstacle dynamic information from each of a plurality of vehicles; a map data acquisition unit acquiring map data including a shape of a traffic lane; a dynamic information management unit associating the dynamic information and the map data with each other and managing them; an interference prediction unit predicting whether or not the subject vehicle interferes with the other vehicle traveling in front of the subject vehicle along a traffic lane adjacent to a traffic lane along which the subject vehicle travels when the other vehicle avoids the obstacle based on the dynamic information and the map data; and a drive assist information generation unit generating drive assist information based on the predicted result.

The present invention extends to methods, systems, and computer program products for detecting hazards in anticipation of opening vehicle doors. Vehicle sensors (e.g., rear viewing cameras) can be used to detect and classify traffic, for example, as pedestrians, bicyclists, skateboarders, roller skaters, wheel chair, etc., approaching on the side of a vehicle. When there is a possibility of a vehicle occupant opening a door into approaching traffic, a warning can be issued in the vehicle cabin to alert vehicle occupants of the approaching traffic. In one aspect, a vehicle prevents a door from opening if opening the door would likely cause an accident.

An approach is provided for determining bicycle lane deviations for autonomous vehicle warning or operation. The approach, for example, involves retrieving probe data associated with a bicycle transportation mode. The approach also involves determining a plurality of probe points of the probe data that are map-matched outside of a bicycle lane. The approach further involves clustering the plurality of probe points into at least one location cluster. The approach further involves storing the one or more location clusters in a geographic database as respective one or more hazard areas where a plurality of bicycles deviates outside of the bicycle lane. By way of example, the approach can further involve using the at least one location cluster to perform at least one of providing a warning message or determining a driving parameter for an autonomous vehicle.

Disclosed herein are systems and method including a method for managing an autonomous vehicle. The method include providing as first input to a machine learning model a raster image and a vector associated with a context of a scene comprising an autonomous vehicle and a plurality of agents, providing as second input to the machine learning model a planned travel path for the autonomous vehicle, based the first input and the second input, outputting from the machine learning model a plurality of yield/assert predictions, wherein the plurality of yield/assert predictions comprises a respective yield/assert prediction related to whether to yield or to assert in relation to each respective agent of the plurality of agents and causing the autonomous vehicle to travel along the planned travel path while yielding or asserting against the plurality of agents according to the plurality of yield/assert predictions.

The technology relates to autonomous vehicles suffering a breakdown along a roadway. Onboard systems may utilize various proactive operations to alert specific vehicles or other objects on or near the roadway about the breakdown. This can be done alternatively or in addition to turning on the hazard lights or calling for remote assistance. The disabled vehicle is able to detect nearby and approaching objects. The detection may be performed in combination with a determination of the type of object or predicted behavior for that object, enables the vehicle to generate a targeted alert that can be transmitted or otherwise presented to that particular object. This approach provides the other object, such as a vehicle, bicyclist or pedestrian, sufficient time and information about the breakdown to take appropriate corrective action. Different communication options are available and may be selected based on the particular object, environmental conditions and other factors.

A cross-traffic warning system for a motor vehicle includes first and second input devices transmitting associated first and second input signals for first and second detected objects positioned on the roadway. The system further includes a computer having one or more processors and a computer readable medium storing instructions. The processor is programmed to determine that the first object is a Vulnerable Road User (“VRU”) travelling on a first path based on the first input signal. The processor is further programmed to determine that the second object is a third party vehicle and further that the VRU and the third party vehicle are travelling on an associated one of first and second paths to imminently collide with one another based on the first and second input signals. The processor is further programmed to generate an actuation signal in response to the processor determining the imminent collision.