An embodiment mobility guidance system includes a sensor provided in a mobility and configured to capture a driving video or an image to transmit the driving video or the image, a memory configured to store a danger zone image, a detector configured to compare the driving video or the image captured by the sensor with the danger zone image stored in the memory to detect an existence of a danger zone on a driving path of the mobility, and a guide configured to set a guide zone in response to the detector detecting the existence of the danger zone and a change in a speed or an acceleration of the mobility, and to provide the guide zone to a second mobility.

An embodiment mobility guidance system includes a sensor provided in a mobility and configured to capture a driving video or an image to transmit the driving video or the image, a memory configured to store a danger zone image, a detector configured to compare the driving video or the image captured by the sensor with the danger zone image stored in the memory to detect an existence of a danger zone on a driving path of the mobility, and a guide configured to set a guide zone in response to the detector detecting the existence of the danger zone and a change in a speed or an acceleration of the mobility, and to provide the guide zone to a second mobility.

A dock assembly is provided. The dock assembly is configured for docking with a robot. An alignment platform of said dock assembly is configured to receive a sweeper module from the robot when the robot is docked and said sweeper module disengages from the robot. The alignment platform has a plurality of cones positioned on a top side of the alignment platform. The plurality of cones are configured to engage a plurality of holes positioned on an underside of the sweeper module when the sweeper module becomes disengaged from the robot. The plurality of cones enable self-alignment of the alignment platform to the sweeper module as the plurality of cones engage the plurality of holes. The alignment platform has a plurality of support pads positioned on a bottom side of the alignment platform. The support pads are configured to rest on a plurality of bearings that permit lateral movement of the alignment platform when the plurality of cones engage the plurality of holes and the alignment platform self-aligns to the sweeper module.

The current disclosure provides techniques for using human driving behavior to assist in decision making of an autonomous vehicle as the autonomous vehicle encounters various scenarios on the road. For each scenario, a model may be generated based on human driving behavior that governs how an autonomous vehicle maneuvers in that scenario. As a result of using these models, reliability and safety of autonomous vehicle may be improved. In addition, because the model is programmed into the autonomous vehicle, the autonomous vehicle, in many instances, need not consume resources to implement complex calculations to determine driving behavior in real-time.

A method for vehicle (V) environment mapping, comprising the operations of: receiving a set of input values from a plurality of sensors, applying temporal fusion processing to the set of input values, resulting in a respective set of occupancy grid maps, applying data fusion processing to the set of occupancy grid maps, resulting in at least one fused occupancy grid map, detecting discrepancies by comparing occupancy grid maps in the set of maps, resulting in a set of detected discrepancies, processing the at least one fused occupancy grid map and outputting a fused occupancy grid map of drivable spaces. The processing operation includes the step of performing an arbitration of conflict in the at least one fused occupancy grid map. The compound fused occupancy grid map of drivable spaces is supplied (IA) to a user circuit, such as a drive assistance interface.

A demarcating system is discussed that includes a control system, a wire loop arrangeable by a user to indicate to an object path as part of a boundary of an area: a signal generator controlled by the control system, wherein voltage signals applied by the signal generator to the loop are controlled by the control system; and current sensing circuitry that senses current signals present within the loop wherein processors of the control system analyse such current signals andoperate in a calibration mode to: cause the signal generator to apply to the loop a series of test voltage waveforms that generate corresponding current waveforms within the loop; and analyse the corresponding current waveforms to determine an operating voltage waveform that, when applied to the loop, generates a corresponding operating current waveform with substantially the same shape as a predetermined current waveform.

A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

A controller for an autonomous vehicle may include: one or more processors configured to: determine a maneuver planned for the vehicle based on a safety driving model and based on a first message from a network component external to the vehicle, the first message including a respective assessment for each proposed maneuver of at least two maneuvers proposed for the vehicle, and provide an in-vehicle instruction to perform the maneuver planned for the vehicle.

Techniques for representing sensor data and predicted behavior of various objects in an environment are described herein. For example, an autonomous vehicle can represent prediction probabilities as an uncertainty model that may be used to detect potential collisions, define a safe operational zone or drivable area, and to make operational decisions in a computationally efficient manner. The uncertainty model may represent a probability that regions within the environment are occupied using a heat map type approach in which various intensities of the heat map represent a likelihood of a corresponding physical region being occupied at a given point in time.

Aspects of the disclosure relate to evaluating pullovers for autonomous vehicles. In one instance, a set of potential pullover locations within a predetermined distance of a destination may be identified. Whether any of the potential pullover locations of the set include one or more of a plurality of predetermined types of regions of interest where a vehicle should not park for an extended period of time may be determined. A pullover location is identified based on the determination. The identified pullover location may be compared to a pullover location identified by autonomous vehicle control software in order to evaluate the pullover location identified by the autonomous vehicle control software.