The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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own viable path candidates using the interpolated path as a 
reference. Utilizing this approach, control points, and spline 
angle vectors that adjust to specify the location and orientation 
of a path. 
4. THE 2007 DARPA URBAN GRAND 
CHALLENGE - THE CHALLENGE OF A DYNAMIC 
ENVIRONMENT 
The 2005 Grand Challenge pushed participants to develop 
solutions for terrain perception and obstacle avoidance which 
required obstacle detection and avoidance at ranges of 40m 
directly in front of the vehicle. This next iteration of the Grand 
Challenge in an urban environment pushed the state of the art 
not only in perception, but object prediction and autonomous 
vehicle interaction in dynamic environments. Teams needed to 
contend with spurious GPS conditions in some areas of the 
course, ensuring the robots followed the rules of the road and 
interaction with 50 manned vehicles simulating traffic along 
with the other robots attempting to finish DARPA’s three 
mandated mission tests. Each of these missions tested different 
core skills (parking, traffic collision avoidance, driving 
precision) and needed to be completed in the shortest amount of 
time. The intention of each of the missions was to test how the 
vehicles interacted with situations human drivers encounter on a 
regular basis. For example, there were several intersections 
where four vehicles were waiting at a stop light. The 
autonomous vehicle needed to know not only when it was safe 
to proceed, but deal with other vehicles which may malfunction 
and need to get out of the way while taking into consideration 
other traffic and the rules of the road. This required a new 
breed of sensors which provided the vehicle with a 360 
representation of its surroundings. Such capability is shown in 
Figure 9. 
Figure 9: 360 degree scanning LIDAR for situational awareness 
The Teams demanded very high performance from their 
positioning and orientation systems. Pose estimation was 
critical to perception, planning, control and providing key data 
to the drive-by-wire systems of autonomous vehicles. As 
demonstrated in the following architecture from Tartan Racing, 
accurate position and orientation estimation was essential to the 
perception and world modeling routines constructed by the 
robot. As discussed previously, data fusion is a key factor in 
determining how successful the robot is in characterizing and 
interacting within its environment to achieve its mission. Pose 
estimation is provided to the behavior generation and motion 
planning routines which are bounded by the mission planning 
parameters programmed into the vehicle. In each phase of the 
mission, the robot needed to integrate the composite 
representation of the world and understand what were safe and 
unsafe maneuvers given the changing targets around it impeding 
its route. 
Given the missions and skills needed to be demonstrated by the 
robots, the benefits of pre-planning were not as profound as 
with the 2005 Challenge. Teams did require a substantial 
amount of data up-sampling from the sparse points provided in 
the DARPA RNDF file. However, instead of having 2 hours to 
prepare the vehicle, Teams only had 15 minutes between 
missions to prepare for the next portion of the race. Route 
planning was absolutely critical to finish the missions in the 
least amount of time, however the missions required much more 
processing of real time obstacle avoidance rather than following 
exact waypoints. This required high bandwidth, low latency 
data to be constantly available to the system especially for 
dynamic data fusion. Detecting a static obstacle is a simple 
process of determining where the target is located, what the lane 
corridor as defined by the Robot’s sensors as compared to the 
RNDF is and what is the safest speed and steering angle around 
the obstacle to avoid it. 
Figure 10: Tartan Racing Architecture [9] 
Figure 10: Vehicle Tracking and Prediction 
When the obstacle is dynamic, there are three fundamental 
challenges. The first is reliable position tracking relative to 
where the vehicle is and where it needs to go (in the local 
coordinate). Second, with accurate range and target bearing the 
robot can determine what lane the obstacle is in from the route 
network definition file (RNDF) or if it is off the road. This