Denso International America, Inc.

Denso Corporation

Robotwits LLC

Yunfei Xu

Takashi Bando

Haris Volos

Ravi Akella

Maxim Likhachev

Ellis Ratner

Lu Lyu

Takafumi Okayasu

Jonathan Michael Butzke

An example computer system includes memory hardware configured to store a machine learning model and historical driving data vector inputs, and processor hardware configured to execute instructions including training the machine learning model with the historical driving data vector inputs to generate a reconstructed driving data output, wherein the reconstructed driving data output includes at least one reconstruction error score indicative of a likelihood that a driving data input corresponds to a desired driving behavior, obtaining a current driving data input, and supplying the current driving data input to the machine learning model to generate a reconstruction error score based on the current driving data input. The instructions may include determining a driving score according to the reconstruction error score, identifying at least one driver notification according to the determined driving score, and transmitting the identified at least one driver notification to a computing device or display.

Systems and machine learning models for automated vehicle monitoring

A system for setting parameters within a first vehicle for a first user of the first vehicle is provided and includes a memory, transceiver, and a processing module. The memory stores normalized parameters, which are specific to the first user. Each of the normalized parameters is a parameter of the vehicle. The transceiver receives the normalized parameters from a mobile device of the first user, a server in a cloud-based network, or a portable memory device. The processing module: determines whether the first user is authorized for parameter translation services; if the first user is authorized for parameter translation services, translates the normalized parameters to resultant parameters; generates parameter recommendations based on the resultant parameters; presents the parameter recommendations to the first user; and adjusts current parameters of the first vehicle based on the resultant parameters and a received response from the first user with regards to the parameter recommendations.

Vehicle recommendation and translation system for setting personalized parameters within vehicles of mobility sharing environments

Autonomous driving systems and methods include a controller of a first vehicle configured to detect an operating state of a second vehicle proximate the first vehicle, and to predict, based on the operating state of the second vehicle, a potential behavior for the second vehicle that optimizes a cost function from the perspective of the second vehicle. The controller then controls the first vehicle to avoid a collision with the second vehicle assuming the second vehicle operates according to the potential behavior.

Vehicle driving system

An autonomous drive system for a vehicle includes at least one environment perception sensor, a perception module, a location module, an intention prediction module, and a control module. The perception module is configured to receive signals from the at least one environment perception sensor and detect and track at least two remote vehicles in one or both of a current lane and a neighboring lane. The location module is configured to determine a location of the vehicle. The intention prediction module is configured to generate predicted trajectories for the at least two remote vehicles. The control module is configured to receive signals from the at least one environment perception sensor and receive the predicted trajectories from the intention prediction module. The control module determines a location and time for a lane change based on the predicted trajectories and controls the vehicle to change lanes at the determined location and time.

System for autonomous lane merging

An automatic external incident detection and reporting system for a vehicle includes at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle or object position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the cameras and proximity sensors, detect whether an external incident has occurred, and determine a type of incident. The remote vehicle or object position determination unit is configured to receive signals from the at least one of the cameras and proximity sensors and determine an incident location. The communication unit is configured to transmit at least the incident location and the type of incident to remote vehicles, to a local access point, or to share the incident location and the type of incident through a crowd-sourcing manner if the external incident has occurred.

Automatic crowd sensing and reporting system for road incidents

An automatic traffic incident detection and reporting system for a vehicle may include at least one of a plurality of cameras and a plurality of proximity sensors, an incident determination unit, a remote vehicle position determination unit, and a communication unit. The incident determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors, detect whether incident involving at least one remote vehicle has occurred, and categorize the incident. The remote vehicle position determination unit is configured to receive signals from the at least one of the plurality of cameras and the plurality of proximity sensors and determine a location of the incident. The communication unit is configured to transmit data related to the incident to at least one of a cloud server and an emergency service provider.

Automatic traffic incident detection and reporting system

A system and method are provided and include a subject vehicle having vehicle actuation systems and vehicle sensors. A planning system includes a global route planner module, an inference module, a motion planner module, and a trajectory follower module. The inference module receives a route from the global route planner module and dynamic obstacles data from a perception system and determines a total cost for different sets of motions associated with different trajectories for traveling along the received route. The total cost includes an inferred cost based on a probability of the associated set of motions having an increased or decreased cost based on the dynamic obstacles data. The motion planner selects a particular set of motions based on the total costs and generates a smooth trajectory for the vehicle. The trajectory follower module controls the vehicle actuation systems based on the smooth trajectory.

Inference-aware motion planning

A system and method are provided and include a subject vehicle having vehicle actuation systems. A driving context database stores driving trends associated with driving locations. A state estimation module determines a current location of the subject vehicle. A global route planner module determines a route to an inputted destination. An action primitive planning module retrieves a driving trend associated with the current location, selects a sequence of action primitives based on the driving trend associated with the current location and based on the determined route, and generates waypoints for the subject vehicle to travel to based on the sequence of action primitives, each waypoint including location coordinates and a direction. A trajectory planner module determines a trajectory for the subject vehicle based on the waypoints. A vehicle control module controls the vehicle actuation systems based on the determined trajectory.

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Milpitas, CA, United States of America

Yunfei Xu