Abstract: In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

Abstract: Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

Abstract: Techniques for generation of an optimal trajectory for a vehicle include receiving, using one or more processors of the vehicle, an instruction for the vehicle to travel from an initial spatiotemporal location to a destination spatiotemporal location. One or more processors are used to generate a trajectory including travel segments. The trajectory begins at the initial spatiotemporal location and terminates at the destination spatiotemporal location. Each travel segment begins at a first spatiotemporal location and terminates at a second spatiotemporal location. Each travel segment is associated with operational metrics. The operational metrics are associated with navigating the vehicle from the first spatiotemporal location to the second spatiotemporal location. Each operational metric is optimized across the travel segments to generate the trajectory.

Abstract: In an example method, a computer system receives first data representing a plurality of scenarios for estimating a performance of a vehicle in conducting autonomous operations. Further, the computer system determines, for each of the scenarios: (i) a first metric indicating an observed performance of the vehicle in that scenario, where the first metric is determined based on at least one rule, and (ii) a second metric indicating a degree of information gain associated with that scenario. The computer system selects a subset of the scenarios based on the first metrics and the second metrics, and outputs second data indicative of the subset of the scenarios.

Abstract: Technology described in this document can be embodied in a method that includes generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cells. The cells are occupied by particles representing at least one of: an object, or a free space in the environment. Sensor data indicative of a state of at least one particle is received, and an update to a time-varying particle density function associated with a location of the particle in the dynamic occupancy grid is calculated from the sensor data and one or more models. The method further includes generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Methods for operating a vehicle in an environment include receiving light detection and ranging (LiDAR) data from a LiDAR of the vehicle. The LiDAR data represents objects located in the environment. A dynamic occupancy grid (DOG) is generated based on a semantic map. The DOG includes multiple grid cells. Each grid cell represents a portion of the environment. For each grid cell, a probability density function is generated based on the LiDAR data. The probability density function represents a probability that the portion of the environment represented by the grid cell is occupied by an object. A time-to-collision (TTC) of the vehicle and the object less than a threshold time is determined based on the probability density function. Responsive to determining that the TTC is less than the threshold time, a control circuit of the vehicle operates the vehicle to avoid a collision of the vehicle and the object.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.

Abstract: Among other things, we describe techniques for redundancy in autonomous vehicles. For example, an autonomous vehicle can include two or more redundant autonomous vehicle operations subsystems.

Abstract: Technology described in this document can be embodied in a method that includes generating, using one or more processing devices of a vehicle operating in an environment, a discretized representation of the environment, including a plurality of cells. The cells are occupied by particles representing at least one of: an object, or a free space in the environment. Sensor data indicative of a state of at least one particle is received, and an update to a time-varying particle density function associated with a location of the particle in the dynamic occupancy grid is calculated from the sensor data and one or more models. The method further includes generating a prediction of occupancy of at least one cell in the discretized representation based on the updated particle density function, and operating the vehicle, using a controller circuit of the vehicle, based at least in part on the prediction.

Abstract: Among other things, a system provides speed behavior planning for vehicles with autonomous driving capabilities.