Denso Corporation

Denso International America, Inc.

Nara Institute of Science and Technology

Robotwits LLC

Waymo LLC

The Ritsumeikan Trust

Takashi Bando

Yunfei Xu

Kazuhito Takenaka

Haris Volos

Maxim Likhachev

Ellis Ratner

Masumi Egawa

Naoki Fukaya

Ravi Akella

Mikio Shimizu

Shin Ishii

Tomohiro Shibata

Lu Lyu

Jonathan Michael Butzke

Hideaki Misawa

Masataka Mori

Takayuki Miyahara

Kenji Konishi

Hossein Tehrani Niknejad

Tadahiro Taniguchi

Akihito Iwai

Takatomi Kubo

Ryunosuke Hamada

Kazushi Ikeda

Savio Joseph Pereira

Abhinav Srinivas Sai Kiran Kunapareddy

A multi-dimensional trajectory planning system is disclosed that includes planning and actuator modules. The planning module executes the planning application to: determine a first dimensionality including first dimensions for a first stage, where each of the first dimensions are active, and where the first dimensions include two or more dimensions; determine a second dimensionality including second dimensions for a second stage, where the second dimensions include the first dimensions or a subset of the first dimensions, and where the second stage has a lower level of dimensionality than the first stage; based on map data and sensor data, estimates first possible future states of the first dimensions for the first stage, and estimates second possible future states of the second dimensions for the second stage based on the first possible future states; and selects a trajectory plan based on the second possible future states.

Multi-dimensional extended trajectory planning with changing dimensionality based on horizon steps

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

A central latency system includes a communication device configured to exchange data with an external device. The data includes current latency data, current contextual data associated with the latency data, or a combination thereof. The system further includes a controller configured to aggregate latency data in response to acquiring the current latency data, generate a latency characterization information based on the aggregated latency data, and transmit the latency characterization information to the external device.

System and method for collaborative centralized latency characterization

A vehicle application enabling system is provided and includes a memory and initialization, latency evaluation, and application enable modules. The initialization module: receives a maximum network latency; sets a percentage of occurrences that the maximum network latency is not satisfied, a maximum false positive rate, and a maximum deviation value; and calculates a weighting factor based on the percentage of occurrences, maximum false positive rate and maximum deviation value. The latency evaluation module implements a latency evaluation algorithm, which includes: comparing one or more latency estimates to the maximum network latency to provide one or more samples; updating confusion matrix statistics based on the one or more samples; updating a probability threshold based on the maximum false positive rate; updating weighted observations based on the weighting factor; and determining a predicted decision based on the probability threshold. The application enable module executes the vehicle application based on the probability threshold.

Application specific connected service provision system

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 mobile network device is provided and includes a memory and a control module. The memory is configured to store a driver assistance application. The control module is configured to execute the driver assistance application and when executing the driver assistance application the control module is configured to: receive mobile sensor signals from a plurality of sensors in the mobile network device; normalize the mobile sensor signals to generate normalized signals; translate the normalized signals to a plurality of time sequences representing vehicle behavior; concatenate the plurality of time sequences to generate pseudo-vehicle behavior signals; and generate driver assistance signals based on the pseudo-vehicle behavior signals to assist a driver of a vehicle.

Vehicle behavior and driver assistance modules for a mobile network device implementing pseudo-vehicle behavior signal generation based on mobile sensor signals

A drive assist apparatus includes a data collection part that collects driving behavior data representing driving maneuvers and vehicle behaviors caused by the driving maneuvers for each drivers, a classification part that classifies the driving behavior data into a plurality of clusters each showing a tendency of driving behavior of the drivers by clustering the driving behavior data, a storage part that stores cluster information representing a driving behavior characteristic of each of the clusters, a subject data acquisition part that acquires, as subject data, the driving behavior data for a subject driver to be assisted, an estimation part that estimates, as a corresponding cluster, one of the clusters to which the subject driver is assumed to belong by comparing the subject data with the cluster information stored in the storage part, and an assist providing part that provides a drive assist depending on the estimated corresponding cluster.

Drive assist apparatus

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 drive data collection system includes an in-vehicle system and an information center. The in-vehicle system includes a data collector that repeatedly collects measurement values indicative of various indexes regarding a state of a subject vehicle, a model memory that memorizes model information regarding discretization rules that are shared by each of participating vehicles in the system, and a data discretizer that discretizes drive data, which includes time-series measurement values collected by the data collector, into multiple data parts according to the model information. The information center includes a data accumulator that accumulates the discretized drive data in a server. The in-vehicle system sends the discretized drive data to the information center through a communicator. Therefore, the drive data collection system efficiently collects the drive data in a versatilely-utilizable manner for the analysis of general driving practices/behaviors.

Drive data collection system

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.

Data-driven planning for automated driving

In a system, a behavior description generator extracts a feature portion from at least one of segmented behavior-data sequences corresponding to at least one target driving situation included in sequential driving situations, and generates behavior-description information indicative of a description of a driver's behavior associated with the at least one target driving situation. An event description generator sequentially detects traffic events in at least one environmental data sequence indicative of time-series driving environments of a vehicle. The event description generator generates event-description information including a description indicative of each of the detected traffic events. A context description generator extracts, from the sequentially detected traffic events, one target traffic event that is detected immediately adjacent to the feature portion. The context description generator generates context information indicative of a context between behavior-description information and the event-description information about the target traffic event.

Driving context generation system for generating driving behavior description information

In an object identification device, each score calculator extracts a feature quantity from the image, and calculates a score using the extracted feature quantity and a model of the specified object. The score represents a reliability that the specified object is displayed in the image. A score-vector generator generates a score vector having the scores as elements thereof. A cluster determiner determines, based on previously determined clusters in which the score vector is classifiable, one of the clusters to which the score vector belongs as a target cluster. An object identifier identifies whether the specified object is displayed in the image based on one of the identification conditions. The one of the identification conditions is previously determined for the target cluster determined by the cluster determiner.

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Mountain View, CA, United States of America

Takashi Bando