Abstract: A method for controlling an autonomous vehicle includes navigating the autonomous vehicle based on a set of rules. The method also includes identifying an abnormality in a current driving situation. The method further includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the abnormality. The method still further includes controlling the autonomous vehicle to violate one or more rules of the set of rules in response to an indication of a successful interaction with the driver.

Abstract: An artificial intelligence perception system for detecting one or more objects includes one or more processors, at least one sensor, and a memory device. The memory device includes an image capture module, an object identifying module, and a logical scaffold module. The image capture module and the object identifying module cause the one or more processors to obtain sensor information of a field of view from a sensor, identify an object within the sensor information, and determine at least one property of the object. The logical scaffold module causes the one or more processors to determine, by a logical scaffold, when the at least one property of the object as determined by the object identifying module is one of a true condition or a false condition.

Abstract: A method for certified control of a self-driving ego vehicle is described. The method includes analyzing a safety situation of the self-driving ego vehicle to determine a proposed vehicle control action using a main controller of the self-driving ego vehicle. The method also includes presenting, by the main controller, the proposed vehicle control action to an interlock controller, including a certificate of the proposed vehicle control action. The method further includes checking a safety certification evidence from the certificate by the interlock controller using a predefined safety argument to verify the safety certification evidence of the certificate. The method also includes directing, by a low-level controller, the self-driving ego vehicle to perform a certified vehicle control action.

Abstract: System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.

Abstract: A method for synthesizing parameters for control of a closed loop system based on a differentiable simulation model of the closed loop system includes determining requirements/specifications for the closed loop system in signal temporal logic (STL). The method also includes selecting a parametric control law having a differentiable parameter control function. The method also includes converting the requirements in signal temporal logic into differentiable computational graph. The method further includes building the differentiable simulation model as a differentiable computational graph. Furthermore, the method includes automatically learning values of parameters for the differentiable parameter control function of the closed loop system by backpropagating an error.

Abstract: A personalized notification method for a mobility as a service (MaaS) vehicle includes receiving conditional personalized notification features from a passenger of the MaaS vehicle. The method also includes monitoring current driving environment of the MaaS vehicle to determine whether a condition of the conditional personalized notification features is satisfied. The method further includes notifying the passenger when the condition is satisfied via at least one localized output device in a compartment of the MaaS vehicle.

Abstract: A method for controlling an autonomous vehicle includes navigating the autonomous vehicle based on a set of rules. The method also includes identifying an abnormality in a current driving situation. The method further includes prompting a passenger of the autonomous vehicle to interact with a driver of a first vehicle in response to identifying the abnormality. The method still further includes controlling the autonomous vehicle to violate one or more rules of the set of rules in response to an indication of a successful interaction with the driver.

Abstract: System and method to autonomously operate a vehicle during travel are disclosed.

Abstract: System, methods, and other embodiments described herein relate to improving situational awareness of a user. In one embodiment, a method includes, in response to acquiring, in a personal device, sensor data from a set of sensors that perceive a surrounding environment of the user, identifying from the sensor data one or more objects in the surrounding environment relative to the user. The method includes determining a threat to the user from the one or more objects according to at least trajectories of the one or more objects embodied in the sensor data. The method includes generating, via the personal device, an indicator to inform the user of the one or more objects according to the threat.

Abstract: System, methods, and other embodiments described herein relate to modeling dynamic agents in a surrounding environment of an ego vehicle. In one embodiment, a method includes, in response to receiving sensor data including present observations of a road agent of the dynamic agents in the surrounding environment, identifying previous observations of the road agent from an electronic data store. The method includes estimating a future state of the road agent using at least the present observations and the previous observations of the road agent to compute the future state according to a probabilistic model comprised of a transition model that accounts for dynamic behaviors of the road agent to characterize transitions between states, and an agent model that accounts for actions of the road agent. The method includes controlling one or more vehicle systems of the ego vehicle according to the future state of the road agent.