Abstract: In one embodiment, a computing system of a vehicle may access sensor data associated with a surrounding environment of a vehicle. The system may generate, based on the sensor data, a first trajectory having one or more first driving characteristics for navigating the vehicle in the surrounding environment. The system may generate a second trajectory having one or more second driving characteristics by modifying the one or more first driving characteristics of the first trajectory. The modifying may use adjustment parameters based on one or more human-driving characteristics of observed human-driven trajectories such that the one or more second driving characteristics satisfy a similarity threshold relative to the one or more human-driving characteristics. The system may determine, based on the second trajectory, vehicle operations to navigate the vehicle in the surrounding environment.

Abstract: A method includes generating a parameter of a trajectory associated with a scenario using a path planner. The parameter is generated based on a training dataset. The method includes comparing the parameter of the trajectory against a validation parameter associated with a validation dataset. The validation parameter is based on human-based vehicle driving trajectory data associated with scenarios that satisfy a level of similarity with the scenario. The method further includes determining a level of similarity between the parameter associated with the scenario and the validation parameter associated with the scenarios, and, subsequent to determining that the level of similarity fails to satisfy a similarity threshold, the method concludes with providing training data associated with the scenario to the training dataset so that a subsequent parameter of a subsequent trajectory generated by the path planner and associated with the scenario satisfies the level of similarity against the validation parameter.

Abstract: Described are street level intelligence platforms, systems, and methods that can include a fleet of swarm vehicles having imaging devices. Images captured by the imaging devices can be used to produce and/or be integrated into maps of the area to produce high-definition maps in near real-time. Such maps may provide enhanced street level intelligence useful for fleet management, navigation, traffic monitoring, and/or so forth.

Abstract: In one embodiment, a method includes, by a computing system associated with a vehicle, determining a current location of the vehicle in a first region, identifying one or more first sets of model parameters associated with the first region and one or more second sets of model parameters associated with a second region, generating, using one or more machine-learning models based on the first sets of model parameters, one or more first inferences based on first sensor data captured by the vehicle, switching the configurations of the models from the first sets of model parameters to the second sets of model parameters, generating, using the models having configurations based on the second sets of model parameters, one or more second inferences based on second sensor data generated by the sensors of the vehicle in the second region, and causing the vehicle to perform one or more operations based on the second inferences.

Abstract: In one embodiment, an apparatus includes a first stage and a second stage. The first stage may include a micro light-directing unit that is operable to receive a light beam from a light source and direct the light beam along one dimension to discrete input locations of a second stage. The second stage may be operable to receive the light beam from the first stage at the discrete input locations along the one dimension and direct the light beam through two dimensions to discrete output locations of the second stage to scan a three-dimensional space.

Abstract: In one embodiment, a method includes receiving a request for evaluating driving data included in a data log, accessing, based on the request, an evaluation configuration file that includes a metric-calculation configuration specifying one or more metric calculators configured to generate one or more output metrics from the driving data and a validation configuration configured to validate the one or more output metrics, instantiating the one or more metric calculators specified by the metric-calculation configuration included in the evaluation configuration file, determining particular driving data from the driving data included in the data log based on the one or more instantiated metric calculators, and generating the one or more output metrics for the particular driving data by using the instantiated one or more metric calculators.

Abstract: In one embodiment, a method includes providing instructions to broadcast a modulated radar chirp signal from a radar antenna of a vehicle. The modulated radar chirp signal includes data associated with the vehicle. The method includes receiving a first return signal whose waveform substantially matches the modulated chirp signal. The first return signal is the modulated radar chirp signal after reflecting off of an object in an environment surrounding the vehicle. The method includes calculating a location for the object using the first return signal, receiving, from a base station antenna, a second return signal that indicates the modulated chirp signal was received by the base station antenna, and providing instructions to establish a wireless communication session with the base station antenna.

Abstract: Described are street level intelligence platforms, systems, and methods that can include a fleet of swarm vehicles having imaging devices. Images captured by the imaging devices can be used to produce and/or be integrated into maps of the area to produce high-definition maps in near real-time. Such maps may provide enhanced street level intelligence useful for fleet management, navigation, traffic monitoring, and/or so forth.

Abstract: Systems, methods, and other embodiments described herein relate to controlling access to an interior of an object by way of static and/or dynamic characteristics of a hand of a user. In an embodiment, a method includes obtain first measurements that are based upon contact of the hand with a handle located on an exterior of an object over a first time period as the hand applies first force to the handle. The method includes identify the user as being authorized to access an interior of the object based upon the first measurements and second measurements, where the second measurements are based upon contact of the hand with the handle over a second time period as the hand applies second force to the handle. The method includes grant the user access to the interior of the object based on the user being authorized.

Abstract: System, methods, and other embodiments described herein relate to controlling access to an interior of an object by way of static and/or dynamic characteristics of a hand of a user. In an embodiment, a method includes obtain first measurements that are based upon contact of the hand with a handle located on an exterior of an object over a first time period as the hand applies first force to the handle. The method includes identify the user as being authorized to access an interior of the object based upon the first measurements and second measurements, where the second measurements are based upon contact of the hand with the handle over a second time period as the hand applies second force to the handle. The method includes grant the user access to the interior of the object based on the user being authorized.