1. Hierarchical Control Systems A Seminar on Artificial Intelligence 1

2. What is HCS? A form of Control System in which a set of devices and governing software is arranged in a hierarchical tree. 2

4. It is organized to divide Decision making responsibility.

5. Each element of the hierarchy is a linked node in the tree.

6. Commands, tasks and goals to be achieved flow down the tree from superior nodes to subordinate nodes.

7. Sensations and command results flow up the tree from subordinate to superior nodes.

8. Nodes may also exchange messages with their siblings.3

10. Their time constraints are relaxed and are capable of reasoning from abstract world model.

11. Lower layers form hybrid intelligent systems, which involves a combination of methods and techniques from AI subfields.

12. They perform local tasks and goals, as planned by higher layers.4

15. A way of decomposing complicated intelligent behavior into many "simple" behavior modules, which are in turn organized into layers.5

17. In November 2007, DARPA held “the Urban Challenge”, which was a prize competition for driverless vehicles.

18. The winning entry, “Tartan Racing” employed a hierarchical control system, with layered mission planning, motion planning, behavior generation, perception, world modeling, and Mechatronics.6

20. This is a particular challenge for vehicle software, as vehicles must make "intelligent" decisions in real time based on the actions of other vehicles.

21. The competition was open to teams and organizations from all around the world.7

23. DARPA will provide the route network 24 hours before the race starts.

24. Vehicles will complete the route by driving between specified checkpoints.

25. Vehicles may “stop and stare” for at most 10 seconds.

26. Vehicles must operate in rain and fog, with GPS blocked.

27. Vehicles must avoid collision with vehicles and other objects such as carts, bicycles, traffic barrels, and objects in the environment such as utility poles.

28. Vehicles must be able to operate in parking areas and perform U-turns as required by the situation. 8

30. Their vehicle “Boss” was a Chevy Tahoe with over 5,00,000 lines of code to autonomously navigate in town and in traffic.

31. Tartan Racing technology enabled Boss to:

32. Follow rules of the road

33. Detect and track other vehicles at long ranges

34. Find a spot and park in a parking lot

35. Obey intersection precedence rules

36. Follow vehicles at a safe distance

37. React to dynamic conditions like blocked roads or broken-down vehicles 9

38. Boss, the Tartan Racing robot, is built on a Chevrolet Tahoe chassis.It incorporates a variety of lidar, radar and visual sensors to safely navigate urban environments. 10

40. Behavior Generation

41. Motion planning

42. Planning in Lanes

43. Planning in Zones

44. Perception & World Modeling

45. Moving Obstacle Fusion

46. Moving Obstacle Tracking

47. Static Obstacle Detection

48. Road Shape Feature Detectors

49. Mechatronics

50. Vehicle Automation

51. Power Electronics

52. Integration and Testing11

53. The Tartan Racing architecture is decomposed into five broad areas: Mission Planning, Motion Planning, Behavior Generation, Perception and World Modeling, and Mechatronics. 12

55. Its component computes:

56. The cost (as function of time and risk) of all possible routes through given knowledge of road networks.

57. The next checkpoint that vehicle must achieve.

58. Optimal path to next checkpoint.

59. It compares routes based on prior knowledge of

60. congestion or blockages

61. construction

62. legal speed limit13

64. Checkpoints are made as nodes and routes between them as directional edges.

65. Costs are assigned to edges based on various factors:

66. Expected time to traverse the edge

67. Length of edge

68. Complexity of local environment14

70. Reason: it allows navigation system to behave correctly if vehicle is unable to perfectly execute the original plan.

71. For e.g.:- if vehicle missed a checkpoint then it can immediately extract the current best path from its current position.

72. As the vehicle navigates, mission planner keeps updating its graph to incorporate newly-observed information.15

74. implemented as a state machine that decomposes mission task into a set of top-level behaviors and their simpler, sub-behaviors.

75. Top-level behaviors:

76. Drive-down road

77. Handle intersection

78. Achieve-zone-pose16

80. Primary sub-behavior is Driving In Lanes (includes distance keeping behavior in presence of a lead vehicle).

83. Establishes a polygonal zone around an intersection and tracks all vehicles within that zone.

84. Those that arrived at stop line before our vehicle are given higher precedence.

85. Once our vehicle determines that its turn to move, it checks for intersection to be clear of obstacles and vehicle (completing their exit or disobeying precedence rules).

86. Then traverse the intersection in a “virtual lane” created by connecting exit point of current lane to entry point in goal lane.18

87. 19

89. Invoked when system needs to traverse a zone and park the vehicle.

90. Also, when vehicle has to find its way, very creatively, out of jammed traffic or from an off-road position to a position where Drive-down road behavior can resume.20

92. Typically involves

93. Either driving down a lane when on roads (Structured Driving)

94. or, navigating through obstacle fields to a desired goal position when in zones. (Unstructured Driving)21

96. From this information a curve representing the centerline of the current lane is computed. This is the nominal path the vehicle should follow.

97. A local motion planner generates trajectories to a set of local goals in order to:

98. Robustly follow the current lane

99. Avoid static and dynamic obstacles.22

101. It is used to compute dynamically feasible trajectories to these local goals.

102. It predicts where the vehicle will end up after following some specified control trajectory.

103. Then control trajectory is optimized to minimize the error between the forwards-simulated vehicle position and the current desired goal position.

104. The resulting trajectories are evaluated against

105. both static and dynamic obstacles in the environment

106. their distance from the centerline path

107. their smoothness and various other metrics.23

109. the road lane provides a preferred position of the vehicle (centerline).

110. no driving lanes in parking lot, thus the movement of the vehicle is less constrained.

111. in zones, very specific goal positions are to be reached.

112. A lattice planner is used that searches over vehicle position and orientation, to plan the path towards goal position.

113. Again, trajectory generator is used to find various paths toward goal.

114. planner searches in a backwards direction, from the goal pose towards the vehicle pose

115. generates feasible high-fidelity maneuvers that are collision-free with respect to the static obstacles observed in the environment.24

117. It quickly generates an initial, suboptimal plan for the vehicle

118. then improves the quality of this solution while deliberation time allows.

119. When new information about the obstacles is received, this algo efficiently repair its existing solution to account for the new obstacle information.

120. This repairing by performing the search in a backward direction

121. This results in a nearly computation-free replanning, when the vehicle deviates from its path due to tracking errors.

122. This lattice planner is flexible enough to be used in a large variety of cases. For e.g.:

123. when navigating in congested intersections

124. to perform U-turns

125. to get the vehicle back on track25

127. Responsible for addressing three critical functions:

128. the detection and tracking of moving obstacles,

129. the detection of static obstacles,

130. estimating the shape of the road.26

132. Objective is to fuse together different information gathered by different sensors to make them coherent.

133. Sensors do measurement of objects they track.

134. A global list is maintained of tracked object by fusion layer.

135. Any unfamiliar obstacle tracked is added as new entry. 27

137. Mobile obstacles are tracked using Extended Kalman Filter to predict and update the state and uncertainty measurement for each tracked object.28

139. Based on this info, it generates a cost map representing “traversability” of the terrain, by comparing pairs of laser points.

140. Based on cost, regions are categorized as “fatal”.29

142. Online Road shape detection is generated using range and intensity data from down looking short range lidars.

143. Intensity change is indicative of painted road lines.

144. relative changes from flat surfaces to raised curbs or drops to soft shoulders are detected by looking for the appropriate geometric features.

145. Algo used are Haar wavelet transforms, heuristic edge detection with adaptive thresholding and dynamic programming methods.30

147. The chassis is a Chevrolet Tahoe was selected due to

148. its integrated electronic interfaces,

149. its high roof giving a good sensor perspective for detecting other vehicles

150. its plentiful room on the inside for both developers and additional electronics.

151. Mechatronics alterations include systems for vehicle automation, auxiliary power, computing, and sensor mounting.31

153. secondary driving controls (turn signals, transmission shifting, etc.) are actuated by a system from Electronic Mobility Controls (EMC).

154. A higher level controller is responsible for vehicle functions such as velocity control and curvature control.32

156. Takes in feedback for steady-state error correction.

157. The speed control structure includes different controllers for throttle and brake, with a switch.

158. Switch is required because:

159. throttle and brake performances of a vehicle are different.

160. The brake system responds much faster to deceleration commands than the throttle system does to acceleration commands.

161. the actuator for throttle and brake is a single motor system.33

163. The auxiliary power system is a high voltage generator that is driven by the engine using a secondary serpentine belt.

164. This generator provides up to 6 kilowatts of power depending on engine load.

165. Integrated Control System is used for this

166. It contains power converters to convert high voltage power into both AC and DC .

167. Manages power output, voltage levels, and temperatures of the generator and converters34

169. Involves incremental testing of each component, system as a whole, as new functions are added or capabilities mature.

170. System subjected to 5 tiers of testing:

171. Unit Testing

172. Subsystem testing with simulated inputs

173. System testing in pure simulation

174. System testing on vehicle with simulation in the Loop

175. System testing on vehicle with live testing35

177. The objective is to verify that the class method or function being tested produces the correct state changes or output for the provided set of inputs.36

179. The process produces output in debug tool for analysis.

180. This helps in much easier integration with rest of system by finding bugs earlier in the process.37

182. These interfaces are simulated by playing back previously logged data such as lidar data, cost maps, vehicle state, or any other interface.

183. On basis of output behavior, analysis is done.38

185. Instead, simulation is used to present simulated moving objects to the real system.

188. It is a snapshot of technology under rapid development. Significant improvements are ongoing in all aspects of overall system performance.40

189. THANK YOU 41