Among other things, techniques are described for expressive vehicle systems. These techniques may include obtaining, with at least one processor, data associated with an environment, the environment comprising a vehicle and at least one object; determining an expressive maneuver including a deceleration of the vehicle such that the vehicle stops at least a first distance away from the at least one object and the vehicle reaches a peak deceleration when the vehicle is a second distance away from the at least one object; generating data associated with control of the vehicle based on the deceleration associated with the expressive maneuver; and transmitting the data associated with the control of the vehicle to cause the vehicle to decelerate based on the deceleration associated with the expressive maneuver.

A method and an apparatus for determining a vehicle speed computing a probability distribution of action intentions based on observation information of a surrounding object. Then, a probability redistribution of the different action intentions is computed based on travel times for the vehicle to travel from a current position to risk areas corresponding to the different action intentions, motion status variations of the surrounding object with the different action intentions are predicted based on the travel times for the vehicle to travel to the risk areas corresponding to the different action intentions. Finally, the travelling speed of the vehicle is determined based on the probability redistribution of the different action intentions, the motion status variations of the surrounding object with the different action intentions, and motion status variations of the vehicle under different travelling speed control actions.

A system includes a first notifying unit, a detection unit, and a second notifying unit. The first notifying unit performs a first notification for notifying a target in a predetermined area of an abnormality, in a case where an abnormal state occurs in a running vehicle. The detection unit detects presence of a target unaware of the first notification. The second notifying unit performs a second notification for notifying the target in the predetermined area of the abnormality, in a case where the presence of the target unaware of the first notification is detected.

An apparatus and method are provided for controlling autonomous driving of a vehicle which may derive predicted paths of a pedestrian and a two-wheel vehicle during autonomous driving of the vehicle so as to minimize accidents. The method includes calculating first height information allocating a first gradient that descends in a proceeding direction of objects, including a vehicle and a pedestrian, from respective positions of the objects based on dynamic information of the objects, calculating second height information allocating a second gradient based on a probability that the pedestrian will occupy infrastructure, calculating final height information by fusing the first height information and the second height information, generating a predicted path of the pedestrian, determining a driving strategy of a host vehicle based on a predicted path of the host vehicle and the predicted path of the pedestrian.

One or more embodiments include techniques for providing an alert, based on monitoring of a driver, via a pedestrian-based vehicle-to-pedestrian (V2P) system associated with a pedestrian. A pedestrian-based V2P system receives, from a vehicle-based V2P system, data related to an awareness level of the driver of the vehicle. The data may include data specifying the awareness level of a driver with respect to a pedestrian, metrics that are determinative of the awareness level of the driver, raw measurement data from the internally facing sensors and/or externally facing sensors, alerts in digitized audio or other suitable format to transmit to the pedestrian, and/or the like. The pedestrian-based V2P system generates an alert based on at least one of a location of the vehicle and the awareness level of the driver of the vehicle. The pedestrian-based V2P system transmits the alert to an output device.

A system for modifying actions taken by an autonomous driving module of an autonomous vehicle may include one or more processors, an input device having an actuator, and a memory having an input module and a modification module. The input module includes instructions that, when executed by the one or more processors, cause the one or more processors to determine when the actuator of the input device is actuated by a passenger of the autonomous vehicle when the autonomous driving module is in an autonomous mode. The modification module includes instructions that, when executed by the one or more processors, cause the one or more processors to modify one or more actions to be performed by the autonomous driving module while maintaining the autonomous mode of the autonomous driving module when the actuator of the input device is actuated by the passenger. The one or more actions may include increasing an amount of sensor data considered by the autonomous driving module.

A system for speed control of electric bikes, electric scooters and road sign recognition for connected vehicles is presented in this invention. A web server system containing all the information on the road signs and traffic lights under the city's jurisdiction with street map is used. This server controls a controller installed on all the road signs and road traffic lights. This controller uses an ISM band transmitter to transmit content of the posted road sign as multiple data packets. These data packets are used by the controller installed in the vehicle to determine type of road signs and then inform the vehicle main system controller to perform appropriate operation for speed control and operation of the vehicle.

A system may receive point cloud data that includes one or more data points associated with an object that was detected by sensors of an autonomous vehicle. The system may identify a subset of the point cloud data having data points that are associated with a likelihood of a pedestrian entering a scene with the object, determine a current probability value using a logistic function that is associated with the subset of the point cloud data, determine, based at least in part on the current probability value, a probability value representing a likelihood of the pedestrian actually being present for the subset of the point cloud data, determine whether the probability value exceeds a false alarm threshold value, and in response to the probability value exceeding the false alarm threshold value, assign data points of the subset an attribute value indicative of the pedestrian being present.

Techniques for estimating ground height based on lidar data are discussed herein. A vehicle captures lidar data as it traverses an environment. The lidar data can be associated with a voxel space as three-dimensional data. Semantic information can be determined and associated with the lidar data and/or the three-dimensional voxel space. A multi-channel input image can be determined based on the three-dimensional voxel space and input into a machine learned (ML) model. The ML model can output data to determine height data and/or classification data associated with a ground surface of the environment. The height data and/or classification data can be utilized to determine a mesh associated with the ground surface. The mesh can be used to control the vehicle and/or determine additional objects proximate the vehicle.

A system and method for mitigating or avoiding risks due to hazards encountered by platooning vehicles. The system and method involve interrogating, with one or more sensors, a space radially extending from a lead vehicle as the lead vehicle travels over the road surface, perceiving the environment within the space, ascertaining a hazard caused by an object in the space, and causing a following vehicle, operating in a platoon with the lead vehicle, to take a preemptive braking action to avoid or mitigate risks resulting from the hazard caused by the object in the space.