Abstract: Virtual bumpers for autonomous vehicles improve effectiveness and safety as such vehicles are operated. One or more sensor systems having a Lidar sensor and a camera sensor determine proximity of objects around the vehicle and facilitate identification of the environment around the vehicle. The sensor systems are placed at various locations around the vehicle. The vehicle identifies an object and one or more properties of the identified object using the sensor systems. Based on the identified object and the properties of the object, a virtual bumper may be created for that object. For example, if the object is identified as another vehicle moving with a certain velocity, the vehicle may determine a minimum space to avoid the other vehicle, either by changing direction or reducing the velocity of the vehicle, with the minimum space constituting a virtual bumper.
Type:
Grant
Filed:
September 20, 2019
Date of Patent:
November 9, 2021
Assignee:
CYNGN, INC.
Inventors:
Michael Lowe, Ain Mckendrick, Andrea Mariotti, Pranav Bajoria, Biao Ma
Abstract: Vehicle sensor systems include modular sensor kits having one or more pods (e.g., sensor roof pods) and/or one or more bumpers (e.g., sensor bumpers). The sensor roof pods are configured to couple to a vehicle. A sensor roof pod may be positioned atop a vehicle proximate a front of the vehicle, proximate a back of the vehicle, or at any position along a top side of the vehicle being coupled, for example, using a mounting shim or a tripod. The sensor roof pods can include sensors (e.g., LIDAR sensors, cameras, ultrasonic sensors, etc.), processing units, control systems (e.g., temperature and/or environmental control systems), and communication devices (e.g., networking and/or wireless devices).
Type:
Application
Filed:
October 15, 2019
Publication date:
June 18, 2020
Applicant:
CYNGN, INC.
Inventors:
Ain MCKENDRICK, Michael W. LOWE, Andrea MARIOTTI, Pranav BAJORIA, Akash JOSHI
Abstract: The localization of a vehicle is determined using less expensive and computationally robust equipment compared to conventional methods. Localization is determined by estimating the position of a vehicle relative to a map of the environment, and the process thereof includes using a map of the surrounding environment of the vehicle, a model of the motion of the frame of reference of the vehicle (e.g., ego motion), sensor data from the surrounding environment, and a process to match sensory data to the map. Localization also includes a process to estimate the position based on the sensor data, the motion of the frame of reference of the vehicle, and/or the map. Such methods and systems enable the use of less expensive components while achieving useful results for a variety of applications, such as autonomous vehicles.
Type:
Application
Filed:
May 24, 2019
Publication date:
May 14, 2020
Applicant:
CYNGN, INC.
Inventors:
I-Chung Joseph LIN, Elena Ramona STEFANESCU, Dhivya SUKUMAR, Sumit SAXENA
Abstract: Virtual bumpers for autonomous vehicles improve effectiveness and safety as such vehicles are operated. One or more sensor systems having a Lidar sensor and a camera sensor determine proximity of objects around the vehicle and facilitate identification of the environment around the vehicle. The sensor systems are placed at various locations around the vehicle. The vehicle identifies an object and one or more properties of the identified object using the sensor systems. Based on the identified object and the properties of the object, a virtual bumper may be created for that object. For example, if the object is identified as another vehicle moving with a certain velocity, the vehicle may determine a minimum space to avoid the other vehicle, either by changing direction or reducing the velocity of the vehicle, with the minimum space constituting a virtual bumper.
Type:
Application
Filed:
September 20, 2019
Publication date:
April 2, 2020
Applicant:
CYNGN, INC.
Inventors:
Michael LOWE, Ain MCKENDRICK, Andrea MARIOTTI, Pranav BAJORIA, Biao MA
Abstract: Artificial intelligence vehicle systems include vehicle guidance systems and adaptive, evolutionary driving training protocols for state machines. A state machine makes decisions based on information supplied by the sensors attached to the vehicle, the current state of the vehicle, the capabilities of the vehicle, and optionally the applicable traffic laws (e.g., if a roadway vehicle) or facility rules (e.g., if a facility vehicle, such as warehouse, construction site, campus, or the like). An autonomous driver of a state machine decides between possible actions given the current environment where those possible actions to existing conditions are represented by action rules, which may be referred to as “genes.” The adaptive systems enable improved vehicle guidance and can improve over time as new circumstances are encountered and processed.