Toyota Research Institute, Inc. (Los Altos, CA)

Systems and methods are available to create trajectories that show a driver’s perspective. Methods include the creation of an ego vehicle’s predicted path for an individual ego vehicle; and the generation at least one road agent predicted route for an external road agency. Once the predicted trajectories have been generated, the method continues by determining that at a minimum one road agent predicted trajectory both (1) is behind an object, indicating travel behind the object, and (2) has an overlap with the object when displayed on an interface that displays a driver’s view. Modifying the predicted road agent trajectory at least once to remove overlap is a part of the method. The method follows by updating a display of the user interface to include any altered road agent predicted trajectory. Systems include a trajectory-prediction module to execute the methods.

This background description serves to give context to the disclosure. Work performed by the currently named inventors, to the extent it can be described in this background section, as well as elements of the description that might not otherwise qualify as prior art at the time of filing is neither explicitly nor implicitly recognized as prior art in relation to present technology.

Vehicle systems are able to predict the trajectory of a vehicle in a range of ways. Sometimes referred to as the “ego-vehicle”, For example, a parallel-autonomy vehicle, such as a vehicle that includes an advanced driver-assistancesystem (ADAS), is a vehicle whose control may be shared between a human driver and an autonomous-driving system. While the human driver maintains the control over certain aspects of driving a vehicle (e.g. steering) however, the ADAS monitors driver actions and can intervene if necessary to prevent the risk of an accident. The ability to predict the course of the ego-vehicle , therefore, is an essential aspect of an ADAS. The vehicle system may show the ego-vehicle’s trajectories in a user-friendly interface.

The vehicle system is also able to anticipate the route taken by one or more road agents outside of a vehicle and show their trajectories on an interface display. Road agents could be any kind of vehicle, which includes bicycles, motorbikes, automobiles and pedestrians. One goal for an autonomous vehicle or parallel-autonomous vehicle is to take in a way that does not collide with the road users it encounters on its way. Since the intentions of road users or their drivers are not typically known with certainty for an autonomous vehicle or to the driver of a vehicle that is parallel-autonomous, knowing the path of a road agent can aid in that goal.

However, when many road agent trajectories are provided on a user interface, it may quickly become overly complicated, especially when the display is provided as a driver’s perspective view (as compared to a top plan view, for example).Accordingly, it would be desirable to provide improved trajectory prediction systems to adequately account for the presence of numerous and/or overlapping trajectories that may be provided on a user interface, which may lead to less complicated displays.

This section is a general overview of the disclosure and is not a comprehensive disclosure of its full scope or all of its features.

In various aspects the present tutorials offer an algorithm for creating trajectories for a vehicle user interface that displays the view of the driver. The system is comprised of at least one processor and memory that is with the processors. The memory stores a trajectory-prediction module including instructions that when executed by the one or more processors cause the one or more processors to perform a series of steps. For example, the trajectory-prediction module mayinclude an instruction to generate an ego-vehicle predicted trajectory for an ego-vehicle, and to generate at least one road agent predicted trajectory for a road agent that is external to the ego-vehicle. The instructions could include a step todetermine that at least one predicted road-agent trajectory (1) exists behind the object indicating travel behind the object, and (2) is overlapping with the object, when displayed on the user interface that displays a driver’s view. Instructions could also include steps to modify the predicted road agent’s trajectory in order to reduce the overlap. A control module may also be provided, including instructions that when executed by processors trigger one or moreprocessors to update the user interface to include any updated road agent’s predicted trajectory.

In another way, the present teachings provide an non-transitory computer-readable medium capable of creating trajectories of a vehicle user interface that displays the view of the driver. A processor or processors could contain instructions that cause the computer-readable medium to generate an ego-vehicle predicted trajectory for an individual ego-vehicle. The processors can also produce at least one predicted route for a road agency which isn’t external to the vehicle’s ego. The instructions may cause a processor to determine that at least one predicted road agent trajectory (1) exists behind an object, indicating that it is behind the object and (2) is overlapping with the object when displayed on the userinterface showing a driver’s perspective view. The instructions may make a processor modify at least one predicted road agent trajectory to remove the overlap. Then, it will create a modified predicted path and refresh the user interface with any modified road agent trajectory.

The present teachings also provide the method for generating the trajectories of the vehicle’s user interface from the perspective of a driver. This method creates an ego vehicle predicted path for an individual ego vehicle. It will also generate at least one road agent predicted route for an agent that is external to the ego. The method is able to determine that at the very least one predicted road agency trajectory (1) exists behind an object. This indicates travel behind the object, and (2) overlaps with it. Modifying the predicted road agent trajectory to eliminate overlap is part of the method. The method proceeds by updating a display of the user interface to include any modified predicted road-agent trajectory.

Other areas of applicability as well as different methods for improving the above technology will become clear from the information provided in this document. The descriptions and examples provided in this summary are as illustrations only and are not intended to limit the scope of the present disclosure.

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