Abstract: Resolving an exception situation in autonomous driving includes receiving an assistance request to resolve the exception situation from an autonomous vehicle (AV); identifying a solution to the exception situation; forwarding the solution to a tele-operator; receiving a request for playback data from the tele-operator; receiving, from the AV, the playback data; and obtaining, from the tele-operator, a validated solution based on the tele-operator using the playback data. The playback data includes snapshots ni of data related to autonomous driving stored at the AV at respective consecutive times ti, for i=1, . . . , N.

Abstract: Exception handing, such as of obstruction situations, by an autonomous vehicle (AV) is disclosed. A method includes identifying an exception situation; identifying a risk associated with autonomously resolving the exception situation; and in response to the risk exceeding a risk threshold, initiating a request for assistance from a tele-operator, and halting for the tele-operator to respond to the request; and receiving a response from the tele-operator.

Abstract: Detection of merge scenarios by an autonomous vehicle (AV) is disclosed. A system includes a memory and a processor. The memory includes instructions executable by the processor to, in response to detecting a merge scenario, identify an interacting object pair including a merging object and a crossing object, where the AV is the merging object; generate a yield hypothesis and a no-yield hypothesis; compute a yield reference path corresponding to the yield hypothesis and a no-yield reference path corresponding to the no-yield hypothesis; determine a yield likelihood of the yield hypothesis and a no-yield likelihood of the no-yield hypothesis; and operate the AV to merge or to wait until the merge scenario is no longer detected based on the yield likelihood and the no-yield likelihood.

Abstract: Trajectory planning for an autonomous vehicle is disclosed. A method for planning a trajectory for an autonomous vehicle includes determining a coarse driveline from a first location to a second location; determining a strategic speed plan for the coarse driveline; removing lateral discontinuities in the coarse driveline based on the strategic speed plan to generate an adjusted coarse driveline; and generating lateral constraints around the adjusted coarse driveline and a discrete-time speed plan based on observed objects.

Abstract: World objects tracking and prediction by an autonomous vehicle (AV) is disclosed. A method includes identifying, based on first observation data received from sensors of the AV, an oncoming vehicle; identifying, based on second observation data received from the sensors of the AV, a road object; generating a lane-following hypothesis for the oncoming vehicle, the lane-following hypothesis indicating an intention that the oncoming vehicle remain in a current road lane; computing a lane-following reference driveline for the lane-following hypothesis of the oncoming vehicle; in response to determining that the lane-following reference driveline is blocked by the road object, generating a go-around hypothesis for the oncoming vehicle, and computing a go-around reference driveline for the go-around hypothesis; and providing at least one of a go-around trajectory corresponding to the go-around hypothesis or a lane-following trajectory corresponding to the lane-following hypothesis.

Abstract: World objects tracking and prediction by an autonomous vehicle (AV) is disclosed. A method includes identifying, based on first observation data received from sensors of the AV, an oncoming vehicle; identifying, based on second observation data received from the sensors of the AV, a road object; generating a lane-following hypothesis for the oncoming vehicle, the lane-following hypothesis indicating an intention that the oncoming vehicle remain in a current road lane; computing a lane-following reference driveline for the lane-following hypothesis of the oncoming vehicle; in response to determining that the lane-following reference driveline is blocked by the road object, generating a go-around hypothesis for the oncoming vehicle, and computing a go-around reference driveline for the go-around hypothesis; and providing at least one of a go-around trajectory corresponding to the go-around hypothesis or a lane-following trajectory corresponding to the lane-following hypothesis.

Abstract: Detection of merge scenarios by an autonomous vehicle (AV) is disclosed. A system includes a memory and a processor. The memory includes instructions executable by the processor to, in response to detecting a merge scenario, identify an interacting object pair including a merging object and a crossing object, where the AV is the merging object; generate a yield hypothesis and a no-yield hypothesis; compute a yield reference path corresponding to the yield hypothesis and a no-yield reference path corresponding to the no-yield hypothesis; determine a yield likelihood of the yield hypothesis and a no-yield likelihood of the no-yield hypothesis; and operate the AV to merge or to wait until the merge scenario is no longer detected based on the yield likelihood and the no-yield likelihood.

Abstract: Trajectory planning for an autonomous vehicle is disclosed. A method for planning a trajectory for an autonomous vehicle includes determining a coarse driveline from a first location to a second location; determining a strategic speed plan for the coarse driveline; removing lateral discontinuities in the coarse driveline based on the strategic speed plan to generate an adjusted coarse driveline; and generating lateral constraints around the adjusted coarse driveline and a discrete-time speed plan based on observed objects.