1. Boss（卡内基·梅隆大学）
各模块的作用如下：
- The Perception subsystem processes sensor data from the vehicle and produces a collection of semantically-rich data elements such as the current pose of the robot, the geometry of the road network, and the location and nature of various obstacles such as road blockages and other vehicles.
- The Mission Planning subsystem computes the fastest route to reach the next check-point from all possible locations within the road network, encoded as an estimated time-to-goal from each waypoint in the network. This estimate incorporates knowledge of road blockages, speed limits, intersection complexity, and the nominal time required to make special maneuvers such as lane changes or U-turns.
- The Behavioral Executive combines the global route information provided by the Mission Planner with local traffic and obstacle information provided by Perception to select a sequence of incremental goals for the Motion Planning subsystem to execute.Typical goals include driving to the end of the current lane or maneuvering to a particular parking spot, and their issuance is predicated on conditions such as precedence at an intersection or the detection of certain anomalous situations.
- The Motion Planning subsystem is responsible for the safe, timely execution of the incremental goals issued by the Behavioral Executive. The isolation of goal selection from goal execution promotes the development of powerful, highly general planning capabilities, which fall into two broad contexts: on-road driving and unstructured driving.A separate path-planning algorithm is used for each context, and the nature and capabilities of each planner have a strong influence on the overall capabilities of the system, including the nature of common failure scenarios and the options for attempting recovery maneuvers.
2. Junior（斯坦福大学）
3. Caroline（布伦瑞克工业大学）
4. Team MIT（麻省理工学院）
各模块的作用如下：
- The Mission Planner tracks the mission state and develops a high-level plan to accomplish the mission based on the RNDF and MDF. The output of the robust minimum time optimization is an ordered list of RNDF waypoints that are provided to the Situational Interpreter. In designing this subsystem, our goal has been to create a resilient planning architecture that ensures that the autonomous vehicle can respond reasonably (and make progress) under unexpected conditions that may occur on the challenge course.
- The Perceptual State Estimator comprises algorithms that, using lidar, radar, vision and navigation sensors, detect and track cars and other obstacles, delineate the drivable road surface, detect and track lane markings and stop lines, and estimate the vehicle’s pose.
- The Local Map API provides an efficient interface to perceptual data, answering queries from the Situational Interpreter and Situational Planner about the validity of potential motion paths with respect to detected obstacles and lane markings.
- The Situational Interpreter uses the mission plan and the situational awareness embedded in the Local Map API to determine the mode state of the vehicle and the environment. This information is used to determine what goal points should be considered by the Situational Planner and what sets of rules, constraints, and performance/robustness weights should be applied. The Situational Interpreter provides inputs to the Mission Planner about any inferred road blockages or traffic delays, and controls transitions amongst different system operating modes.
- The Situational Planner identifies and optimizes a kino-dynamically feasible vehicle trajectory that moves towards the RNDF waypoint selected by the Mission Planner and Situational Interpreter using the constraints given by the Situational Interpreter and the situational awareness embedded in the Local Map API. Uncertainty in local situational awareness is handled through rapid replanning and constraint tightening. The Situational Planner also accounts explicitly for vehicle safety, even with moving obstacles. The output is a desired vehicle trajectory, specified as an ordered list of waypoints (each with position, velocity, and heading) that are provided to the Vehicle Controller.
- The Vehicle Controller uses the inputs from the Perceptual State Estimator to execute the low-level control necessary to track the desired paths and velocity profiles issued by the Situational Planner.
- The Safety Module monitors sensor data, overriding vehicle control as necessary to avoid collisions. This module addresses safety pervasively through its interactions with vehicle hardware, firmware, and software, and careful definition of system operating modes.