# US 12,602,998 B2 · Distributed computing for autonomous vehicles > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,602,998 B2 - Document ID: US12602998B2 - Status: Granted - Kind: B2 - Application number: 18/762,995 - Filed: 2024-07-03 - Published: 2024-10-24 - Granted: 2026-04-14 - Earliest claimed: 2019-08-31 - Assignees: Cavh LLC - Applicants: Cavh LLC - Inventors: Bin Ran; Sicheng Fu; Rui Gan; Yang Cheng; Shen Li; Kexin Tian; Tianyi Chen; Shuoxuan Dong; Kunsong Shi; Haotian Shi; Xiaotian Li - Web page: https://patentgrep.com/patent/US12602998B2 ## CPC - G08G 1/096783: {where the origin of the information is a roadside individual element} - G08G 1/0112: {from the vehicle, e.g. floating car data [FCD]} - G08G 1/0116: {from roadside infrastructure, e.g. beacons} - G08G 1/0133: {for classifying traffic situation} - G08G 1/0141: {for traffic information dissemination} - G08G 1/0145: {for active traffic flow control} - G08G 1/096708: {where the received information might be used to generate an automatic action on the vehicle control} - G08G 1/096725: {where the received information generates an automatic action on the vehicle control} - G08G 1/096741: {where the source of the transmitted information selects which information to transmit to each vehicle} - G08G 1/096775: {where the origin of the information is a central station} - G08G 1/096811: {where the route is computed offboard} - G08G 1/096827: {where the route is computed onboard} - G08G 1/164: {Centralised systems, e.g. external to vehicles} - G08G: TRAFFIC CONTROL SYSTEMS (guiding railway traffic, ensuring the safety of railway traffic B61L; arrangement of road signs or traffic signals E01F9/00; radar or analogous systems, sonar systems, lidar systems specially adapted for traffic control G01S13/91, G01S15/88, G01S17/88; {radar or analogous systems, sonar systems, lidar systems specially adapted for anti-collision purposes G01S13/93, G01S15/93, G01S17/93}) ## Abstract This invention presents an automated driving system with distributed computing (ADS-DC). During the operation of a connected automated vehicle (CAV), some or all of its automated driving capabilities for sensing, prediction, planning, decision-making, or control may be downgraded due to long-tail events or malfunctions. The intelligent roadside toolbox (IRT) functions as an edge server or a cloud, and can supplement CAV's sensing functions, prediction and management functions, planning and decision-making functions, and vehicle control functions by providing customized, on-demand, and dynamic computing resources and functions to the CAV. In addition, the IRT computing functions provide the computation support for sensing, prediction, planning, decision-making, and/or control functions of said CAV. Namely, the IRT functions as an edge server or a cloud to provide processing, training or optimization of CAV driving models as well as facilitate the implementation of the driving models in the CAV. ## Claims ### Claim 1 An automated driving system with distributed computing (ADS-DC) comprising: a) a vehicle onboard system in a connected and automated vehicle (CAV), which is configured to conduct sensing and decision-making, and generate control instructions for automated driving of the CAV; b) an intelligent roadside toolbox (IRT), wherein said IRT provides customized, on-demand, and dynamic computing resources and functions to the CAV; and c) a communications media for transmitting data between said CAV and said IRT, wherein said IRT computing resources and functions are configured to support the CAV's sensing functions, prediction and management functions, planning and decision-making functions, and vehicle control functions. ### Claim 2 (depends on 1) The system of claim 1, wherein said IRT resources and functions are provided by a cloud. ### Claim 3 (depends on 1) The system of claim 1, wherein the IRT provides computing resources and functions for the CAV to avoid trajectory conflicts with other vehicles and/or to adjust vehicle route and/or trajectory for driving environments including snow, sleet, fog or other adverse weather or road conditions. ### Claim 4 (depends on 1) The system of claim 1, wherein the IRT computing resources and functions improve safety and stability of the CAV by assembling IRT computing resources and providing IRT computing resources and functions to the CAV. ### Claim 5 (depends on 1) The system of claim 1, wherein the IRT computing resources and functions supplement the CAV according to vehicle manufacturer designs to improve CAV performance. ### Claim 6 (depends on 1) The system of claim 1, wherein said system is configured to provide supplemental computing resources and functions to the CAV in response to a value of a vehicle cost exceeding a threshold and/or in response to detecting a component, function, and/or service failure. ### Claim 7 (depends on 1) The system of claim 1, wherein said IRT is configured to provide a customized computing service for vehicle manufacturers and/or driving services providers, said customized computing service comprising computing functions for remote-control service, pavement condition detection, and/or pedestrian prediction. ### Claim 8 (depends on 1) The system of claim 1, wherein said IRT is configured to receive information from a vehicle OBU, electronic stability program (ESP), and/or vehicle control unit (VCU). ### Claim 9 (depends on 1) The system of claim 1, wherein the IRT computing functions provide the computation support for sensing function, prediction function, planning function, decision-making function, and/or control function of said CAV. ### Claim 10 (depends on 9) The system of claim 9, configured to integrate sensor and/or driving environment information from different resources to provide integrated sensor and/or driving environment information and pass said integrated sensor and/or driving environment information to a prediction module. ### Claim 11 (depends on 9) The system of claim 9, wherein said sensing comprises providing information in real-time, short-term, and/or long-term for transportation behavior prediction and management, planning and decision-making, and/or vehicle control. ### Claim 12 (depends on 9) The system of claim 9 wherein said prediction functions provide: i) prediction support comprising providing raw data and/or providing features extracted from raw data; and/or ii) a prediction result, wherein the prediction support and/or a prediction result is/are provided to the CAV based on the prediction requirements of said CAV. ### Claim 13 (depends on 9) The system of claim 9, wherein said prediction functions predict a behavior of surrounding vehicles, pedestrians, bicycles, and/or other moving objects. ### Claim 14 (depends on 9) The system of claim 9, wherein said planning and decision-making functions provide: i) path planning comprising identifying and/or providing a detailed driving path at a microscopic level for automated driving of the CAV; ii) route planning comprising identifying and/or providing a route for automated driving of the CAV; iii) special condition planning comprising identifying and/or providing a detailed driving path at a microscopic level and/or a route for automated driving of the CAV during special weather conditions or event conditions; and/or iv) disaster solutions comprising identifying and/or providing a detailed driving path at a microscopic level and/or a route for automated driving of the CAV during a disaster. ### Claim 15 (depends on 9) The system of claim 9, wherein said vehicle control functions are configured to determine a computation resource supporting automated driving of the CAV and request and/or provide supplemental computation resources from said IRT. ### Claim 16 An automated driving system with distributed computing (ADS-DC) comprising: a) a vehicle onboard system in a connected and automated vehicle (CAV), which is configured to conduct sensing and decision-making, and generate control instructions for automated driving of the CAV; and c) a communications media for transmitting data between said CAV and an intelligent roadside toolbox (IRT), wherein the communications media communicates with the IRT; wherein said IRT provides customized, on-demand, and dynamic computing resources and functions to the CAV; and wherein said IRT computing resources and functions are configured to support the CAV's sensing functions, prediction and management functions, planning and decision-making functions, and vehicle control functions. ### Claim 17 (depends on 16) The system of claim 16, wherein said IRT resources and functions are provided by a cloud. ### Claim 18 (depends on 16) The system of claim 16, wherein said system is configured to provide supplemental computing resources and functions to the CAV in response to a value of a vehicle cost exceeding a threshold and/or in response to detecting a component, function, and/or service failure. ### Claim 19 (depends on 16) The system of claim 16, wherein the IRT computing functions provide the computation support for sensing function, prediction function, planning function, decision-making function, and/or control function of said CAV. ### Claim 20 (depends on 19) The system of claim 19, wherein said prediction functions predict a behavior of surrounding vehicles, pedestrians, bicycles, and/or other moving objects.