A method of detecting a malicious wireless vehicle-to-everything (V2X) communication includes retrieving perception data from a perception system on an ego vehicle, determining information about nearby objects from the perception data, receiving a first Basic Safety Message (BSM) from a first V2X source and determining if a vehicle location indicated in the first BSM corresponds to a location visible to the ego vehicle. In the event that the vehicle location indicated in the first BSM does not correspond to a location visible to the ego vehicle, the method further includes receiving a second V2X communication from a second V2X source and determining if the second V2X communication indicates a vehicle at the vehicle location indicated in the first BSM. In the event that the second V2X communication does not indicate a vehicle at the vehicle location indicated in the first BSM, the first BSM data is flagged as malicious.

Example embodiments relate to vehicle sensor modules with external audio receivers. An example sensor module may include sensors and can be coupled to a vehicle's roof with a first microphone positioned proximate to the front of the sensor module. The sensor module can also include a second microphone extending into a first side of the sensor module such that the second microphone is configured to detect audio originating from an environment located relative to a first side of the vehicle and a third microphone extending into a second side of the sensor module such that the third microphone is configured to detect audio originating from the environment located relative to a second side of the vehicle, wherein the second side is opposite of the first side.

Disclosed are a track mergence module and a method by which track deletion conditions are set to be varied depending on various situations and a track corresponding to the set track deletion conditions is deleted. The track mergence module includes a track deletion condition generation and storage unit, a phenomenon determination unit and a track mergence unit. A track to be deleted may be accurately deleted by generating the track deletion conditions so as to adaptively correspond to the performance of a sensor and various situations in which a host vehicle is placed, thereby being capable of erroneous braking of the autonomously driving host vehicle.

Systems and methods are provided for navigating a host vehicle. A processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle travelling toward the host vehicle; determine a next-state distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a stopping distance for the host vehicle based on a braking profile, a maximum acceleration capability, and a current speed of the host vehicle; determine a stopping distance for the target vehicle based on a braking profile and a current speed of the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the stopping distances for the host vehicle and the target vehicle.

A vehicle configured to alert pedestrians. The vehicle includes a driver-assist system including a memory device, a processor, and at least one camera. The memory device includes instructions which, when executed by the processor, cause the processor to detect, utilizing the at least one camera, at least one pedestrian near the vehicle, determine a proximity of the at least one pedestrian to the vehicle, compare the proximity to a threshold proximity, and automatically emit an audible alert from the vehicle in response to the proximity being less than the threshold proximity.

A method and system for predicting a near future path for a vehicle. For predicting the near future path sensor data and vehicle driving data is collected. Road data is collected indicative of a roadway on the presently occupied road for the vehicle. The sensor data and the vehicle driving data is pre-processed to provide object data comprising a time series of previous positions, headings, and velocities of each of the objects relative the vehicle. The object data, the vehicle driving data, and the road data is processed in a deep neural network to predict the near future path for the vehicle. The invention also relates to a vehicle comprising the system.

A method of controlling an autonomous vehicle, which is movable on a surface, includes obtaining a model (world model) of the surface, by which each area of the surface is associated with a probabilistic occupancy score; determining, on the basis of the area's position, an occupancy threshold to be applied to an area of the surface; enabling movement of the AV into the area if the associated occupancy score is less than the determined occupancy threshold; and otherwise disabling movement into the area. In one embodiment, where the model is obtained or updated based on measurement data from one or more sensors carried by the autonomous vehicle, the occupancy threshold is determined to be relatively lower if the area is outside a field of view of the sensors carried by the AV and relatively higher if the area is inside the field of view.

Techniques are described herein for determining one or more actions for an autonomous vehicle to perform, based on simulation of at least one possible scenario. A possible scenario may involve, for example, the autonomous vehicle interacting with an object in the environment. The possible scenario may be simulated by modifying a first internal map containing information about the autonomous vehicle and the environment. As part of the simulation, one or more parameters of the first internal map can be modified in order to, for example, determine the state of the object at a particular point in the future. Based on the modification of the one or more parameters, a second internal map representing a possible scenario is generated from the first internal map. Both the first internal map and the second internal map can be evaluated to decide which action to take.

Systems and methods are provided for navigating a host vehicle. At least one processing device may be programmed to receive an image representative of an environment of the host vehicle; determine a planned navigational action for the host vehicle; analyze the image to identify a target vehicle in the environment of the host vehicle; determine a next-state lateral distance between the host vehicle and the target vehicle that would result if the planned navigational action was taken; determine a lateral braking distance for the host vehicle and the target vehicle based on a maximum yaw rate capability, a maximum change in turn radius capability, and a current lateral speed of the host vehicle and the target vehicle; and implement the planned navigational action if the determined next-state distance is greater than a sum of the lateral braking distances for the host vehicle and the target vehicle.

A travel control apparatus for a vehicle includes a surrounding environment recognition device, a collision time calculator, a collision object estimator, an after-collision travel range estimator, a collision detector, and a travel control unit. The surrounding environment recognition device includes a recognizer, a collision object recognizer that recognizes an object that has a possibility to come into collision with the vehicle, and a safety degree region setter that sets safety degree regions. The collision object estimator estimates a travel route of the object and a collision position on the vehicle. The after-collision travel range estimator estimates a travel range of the vehicle after the collision based on the estimated collision position. When the collision between the vehicle and the object is detected, the travel control unit performs travel control depending on a safety degree in one of the safety degree regions in front of the travel range after the collision.