According to one embodiment, a system receives a captured image perceiving an environment of an autonomous driving vehicle (ADV) from an image capturing device of the ADV capturing a plurality of obstacles near the ADV. The system generates a first tunnel based on a width of a road lane for the ADV, where the first tunnel represents a passable lane for the ADV to travel through. The system generates one or more additional tunnels based on locations of the obstacles, where the one or more additional tunnels modify a width of the passable lane according to a level of invasiveness of the obstacles. The system generates a trajectory of the ADV based on the first and the additional tunnels to control the ADV according to the trajectory to navigate around the obstacles without collision.

Systems and methods are provided to implement cooperative traffic congestion detection, and enhance the accuracy of detection of traffic congestion for enhanced routing and maneuvering vehicles along a travel route. A vehicle is configured to receive vehicle data from an ad-hoc network of a plurality of vehicles that are communicatively connected (and proximately located). A subset of the plurality of vehicles can be sensor-rich vehicles that are equipped with ranging sensors (e.g., cameras, LIDAR, radar, ultrasonic sensors), which enables real-time detection of the multiple traffic parameters, such as the presence of other vehicles, vehicle speed, vehicle movement, traffic, and the like, within the vicinity along the route. The vehicle employs cooperative traffic congestion detection, and fuses data from the plurality of vehicles, including sensor-rich vehicles and legacy vehicles, and applies a learning-based algorithm, such as a machine-learning (ML) algorithm, to generate a real-time and more accurate estimate of traffic congestion.

A trajectory for an autonomous vehicle (AV) can be generated using curvature segments. A decision planner component can receive a reference trajectory for the AV to follow in an environment. A number of subdivisions (frames) of the reference trajectory may be associated with a curvature value and a tangent vector. Starting with an initial position of the AV, a candidate trajectory can be determined by continuously intersecting a segment with an origin at the initial position of the AV and a reference line associated with a particular frame. The reference line can be substantially perpendicular to the tangent vector of the particular frame. A location of the intersection between the segment and the reference line can be based on a curvature value of the segment. Optimizing a candidate trajectory can include varying curvature values associated with various segments and determining costs of the various candidate trajectories.

Techniques for improving the performance of an autonomous vehicle (AV) are described herein. A system can determine a plan for the AV in a driving scenario that optimizes an initial cost function of a control algorithm of the AV. The system can obtain data describing an observed human driving path in the driving scenario. Additionally, the system can determine for each cost dimension in the plurality of cost dimensions, a quantity that compares the estimated cost to the observed cost of the observed human driving path. Moreover, the system can determine a function of a sum of the quantities determined for each cost dimension in the plurality of cost dimensions. Subsequently, the system can use an optimization algorithm to adjust one or more weights of the plurality of weights applied to the plurality of cost dimensions to optimize the function of the sum of the quantities.

A vehicle control interface includes a control unit configured to: connect between a vehicle platform including a first computer that performs travel control of a vehicle and an automated-driving platform including a second computer that performs automated-driving control of the vehicle and acquire a first control command containing an instruction for the vehicle platform from the second computer; convert the first control command to a second control command for the first computer; and send the second control command to the first computer. The control unit is configured to send, to the second computer, a specifiable range of a physical quantity that is specified by the second computer via the first control command.

Disclosed herein are system and method embodiments to implement a validation of a vector map. The validation process may merge proposed and persisted high-definition mapping data, evaluate the high-definition mapping data with a set of customizable validation rules, return/persist validation results, and provide a means to acknowledge validation failures to minimize creation of problematic vector map content.

When a marker on a road is detected, an automated driving apparatus mounted on a vehicle determines whether to perform automated driving based on comparison between a relative movement log from the marker as a start point according to autonomous navigation and shape point data concerning a lane acquired from the most recent map data.

A vehicle control system includes a recording device configured to record vehicle information in files. The recording device includes: an event information receiving unit configured to receive information about prescribed events; a tag providing unit configured to provide protection tags to the files, the protection tags being of different types according to magnitude of the events; and an overwriting setting unit configured to prohibit overwriting of the files provided with the protection tags of a same type in a case where the number of the files provided with the protection tags of the same type is within a prescribed upper limit number, and to permit the overwriting of the files provided with the protection tags of the same type in a case where the number of the files provided with the protection tags of the same type exceeds the upper limit number.

An occlusion is identified in a vehicle transportation network. A visibility grid is identified on a second side of the occlusion for a vehicle that is on a first side of the occlusion. The visibility grid is identified with respect to a region of interest that is at least a predefined distance above ground. The visibility grid is used to identify first portions of roads sensed by a sensor positioned on the vehicle and second portions of the roads that are not sensed by the sensor. A driving behavior of the vehicle is altered based on the visibility grid.

Autonomous vehicle operation with explicit occlusion reasoning may include traversing, by a vehicle, a vehicle transporation network. Traversing the vehicle transportation network can include receiving, from a sensor of the vehicle, sensor data for a portion of a vehicle operational environment, determining, using the sensor data, a visibility grid comprising coordinates forming an unobserved region within a defined distance from the vehicle, computing a probability of a presence of an external object within the unobserved region by comparing the visibility grid to a map (e.g., a high-definition map), and traversing a portion of the vehicle transportation network using the probability. An apparatus and a vehicle are also described.