Methods and devices for generating data for controlling a Self-Driving Car (SDC) are disclosed. The method includes: i) receiving a section of a road map corresponding to surroundings of the SDC and at least one object, ii) generating a plurality of predicted trajectories including a potential future location points of the at least one object, iii) mapping the potential future location points on the section of the road map, iv) computing a score for each of the potential future location points, the score representing an association of a given potential future location point with the plurality of road lanes at a future instance of time, v) computing an aggregated score from the scores corresponding to potential future location points, and vi) based on the aggregated score, determining a predicted location of the at least one object at the future instance of time.

A method for monitoring a data fusion function of an infrastructure system for the infrastructure-supported assistance of motor vehicles during an at least semi-automated driving task within an infrastructure, the infrastructure including multiple infrastructure surroundings sensors for detecting an area of the infrastructure. The method includes: receiving multiple input data sets intended for the data fusion function, each of which includes surroundings data based on the respective detection of the area, which represent the detected area; receiving output data based on a data fusion of the input data sets, output by the data fusion function; checking the input data sets and/or the output data for consistency; outputting a check result of the check. A device, a computer program, and a machine-readable memory medium are also provided.

A traffic light determining apparatus, a system including the same, and a method thereof, includes: a processor configured to obtain traffic light status information of an intersection while a vehicle is driven, and when obtaining the traffic light status information is not possible, configured to estimate the traffic light status information according to surrounding vehicle information or pedestrian information; and a storage configured to store data and algorithms driven by the processor.

A vehicle control device of an embodiment includes a recognizer configured to recognize a surrounding situation of a vehicle, and a driving controller configured to execute driving control of controlling one or both of a speed and steering of the vehicle on the basis of the surrounding situation recognized by the recognizer, in which the recognizer recognizes a traffic participant present in front of the vehicle and a traffic participant priority section present in a traveling direction of the vehicle, and the driving controller sets a risk area for the traffic participant priority section on the basis of a position and a traveling direction of the traffic participant, and executes the driving control based on the set risk area and the position of the traffic participant.

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.

A refuse vehicle comprising a chassis, a body assembly coupled to the chassis, the body assembly defining a refuse compartment, one or more sensors coupled to the body and configured to provide data relating to the presence of an obstacle within an area near the refuse vehicle, a controller configured to receive the data from the one or more sensors, determine, using an obstacle detector and the data, the presence of an obstacle within the area and initiate a control action, wherein the control action includes at least one of controlling the movement of the refuse vehicle, controlling the movement of a lift assembly attached to the body assembly, or generating an alert.

Embodiments is disclosed to detect a locked heading direction of a pedestrian and to predict a path for the pedestrian using the locked heading direction. According to one embodiment, a system perceives an environment of an autonomous driving vehicle (ADV) using one or more image capturing devices. The system detects a pedestrian in the perceived environment. The system determines a facing direction of the pedestrian relative to the ADV as one of left/right side, front, or back. If the facing direction of the pedestrian is determined to be front or back facing, the system determines a lane nearest to the pedestrian. The system projects the pedestrian onto the nearest lane to determine a lane direction at the projection. The system determines a heading direction for the pedestrian locking to the lane direction of the nearest lane based on a predetermined condition.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for generating unmapped U-turn predictions using a machine learning model. One of the methods includes obtaining features of an agent travelling on a roadway. One or more unmapped U-turn regions in a vicinity of the agent on the roadway are identified. For each of the unmapped U-turn regions and from at least the features of the agent, a respective likelihood score that represents a likelihood that the agent intends to make an unmapped U-turn at the unmapped U-turn region is generated. Based on the respective likelihood scores, one or more of the unmapped U-turn regions are selected. For each selected unmapped U-turn region, data specifying a candidate future trajectory in which the agent makes the unmapped U-turn at the selected unmapped U-turn region is provided as a possible future trajectory for the agent.

Among other things, techniques are described for predicting how an agent (e.g., a vehicle, bicycle, pedestrian, etc.) will move in an environment based on prior movement, the road network, the surrounding objects and/or other relevant environmental factors. One trajectory prediction technique involves generating a probability map for an agent's movement. Another trajectory prediction technique involves generating a trajectory lattice, for an agent's movement. In addition, a different trajectory prediction technique involves multi-modal regression where a classifier (e.g., a neural network) is trained to classify the probability of a number of (learned) modes such that each model produces a trajectory based on the current input.

The present disclosure provides autonomous vehicle systems and methods that include or otherwise leverage a motion planning system that solves gridlock as part of determining a motion plan for an autonomous vehicle (AV). In particular, a scenario generator within a motion planning system can determine one or more keep clear areas associated with the lane sequence, each keep clear area indicative of a region along the nominal path in which gridlock prevention is desired. A gridlock constraint can be generated for each of the one or more keep clear areas, each constraint being defined as a constraint area in a multi-dimensional space. A low-cost trajectory path can be determined through a portion of the multi-dimensional space that minimizes exposure to the constraint areas and that is consistent with all constraints generated for the one or more objects of interest and the one or more keep clear areas.