Motional Ad LLC

Infineon Technologies Ag

Atemito Pte. Ltd

Yu Pan

Scott D Pendleton

Jiong Yang

Thomas Koelbaek Jespersen

James Guo Ming Fu

You Hong Eng

Fan Yung Ma

Guchan Ozbilgin

Lubing Zhou

Giancarlo Baldan

Puneet Singhal

Shu-Kai Lin

Laith Sahawneh

Xiaojun Sun

Jens Groth

Daniel Krause

Joachim Peuckert

Rob Cuzner

Jack Magree

Leong Chye Lee

Giles Matthews

Günther W Diekhöner

Among other things, techniques are described for spatially and temporally consistent ground modelling with information fusion. A vehicle location and orientation is obtained and an instantaneous ground height estimation is obtained for anchor cells of a grid map of the vehicle based on the obtained vehicle location and orientation. A pseudo ground height associated with non-anchor cells of the grid map is computed. A Bayesian filtering framework is used to generate a final ground height estimate for the anchor cells and the non-anchor cells based on the instantaneous ground height estimation for the anchor cells, the pseudo ground height associated with non-anchor cells, an estimated ground height from a previous timestamp, or any combinations thereof. The vehicle operates according to the final ground height estimate.

Spatially and temporally consistent ground modelling with information fusion

Among other things, techniques are described for determining precedence order at a multiway stop. In embodiments, identifications are assigned to tracks, and young tracks are compared to stale tracks. A young track matches a stale track based on one or more factors. An identification of the young track is reassigned to an identification of the stale track, wherein the young track is determined to match the stale track based on the one or more factors. An earliest time of appearance of agents is determined based on identifications and in view of perception obscured areas. A precedence order for navigating through the intersection is determined based on local rules, the identifications, and the earliest time of appearance of agents, and the vehicle proceeds through the multiway stop intersection in accordance with the precedence order.

Precedence determination at multi-way stops

Embodiments for operational envelope detection (OED) with situational assessment are disclosed. Embodiments herein relate to an operational envelope detector that is configured to receive, as inputs, information related to sensors of the system and information related to operational design domain (ODD) requirements. The OED then compares the information related to sensors of the system to the information related to the ODD requirements, and identifies whether the system is operating within its ODD or whether a remedial action is appropriate to adjust the ODD requirements based on the current sensor information. Other embodiments are described and/or claimed.

Operational envelope detection with situational assessment

Provided are methods for learning to identify safety-critical scenarios for autonomous vehicles. First state information representing a first state of a driving scenario is received. The information includes a state of a vehicle and a state of an agent in the vehicle's environment. The first state information is processed with a neural network to determine at least one action to be performed by the agent, including a perception degradation action causing misperception of the agent by a perception system of the vehicle. Second state information representing a second state of the driving scenario is received after performance of the at least one action. A reward for the action is determined. First and second distances between the vehicle and the agent are determined and compared to determine the reward for the at least one action. At least one weight of the neural network is adjusted based on the reward.

Learning to identify safety-critical scenarios for an autonomous vehicle

Among other things, techniques are described for obtaining a range image related to a depth sensor of a vehicle operating in an environment. A first data point is identified in the range image with an intensity at or below a first intensity threshold. A first number of data points are determined in the range image that have an intensity at or above a second intensity threshold in a first region of the range image. Then, it is determined whether the first number of data points is at or above a region number threshold. The first data point is removed from the range image if the first number of data points is at or above the region number threshold. Operation of the vehicle is then facilitated in the environment based at least in part on the range image. Other embodiments may be described or claimed.

Ghost point filtering

A pet container for transporting pets, in particular for air shipment of pets, comprising an outer shell having a top portion and a bottom portion, wherein the outer shell defines a pet chamber for accommodating a pet, wherein the outer shell has an opening formed therein for providing access to the pet chamber and a door for opening and closing the opening, and a climate compartment for receiving a cooling or heating medium therein, wherein the climate compartment is provided below the pet chamber and has an orifice for horizontally inserting a cooling or heating medium into and removing a cooling or heating medium from the climate compartment.

Climatized pet container

Systems, methods, and circuitries are provided to control a gain setting in an analog-to-digital converter (ADC) that converts an analog signal to a digital signal based on a reference voltage. Temperature compensation circuitry includes a temperature gain correction circuitry and a combination circuitry. The temperature gain correction circuitry is configured to determine a correction term based on a temperature that affects the reference voltage. The combination circuitry is configured to combine the correction term with a calibration gain value to generate a corrected calibration gain value and provide the corrected calibration gain value to the ADC to control the gain setting.

Temperature reference gain correction for analog-to-digital converter

Growing community of inventors

Singapore, Singapore

Yu Pan