Carnegie Mellon University
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Real-time Autonomous Car Chaser Operating Optimally at Night (RACCOON)
This project is no longer active.
Head: Rahul Sukthankar
Contact: Rahul Sukthankar
Mailing address:
Carnegie Mellon University
Robotics Institute
5000 Forbes Avenue
Pittsburgh, PA 15213
Associated center(s) / consortia:
 Vision and Autonomous Systems Center (VASC)
Associated lab(s) / group(s):
Project Homepage
Night-time driving poses a number of difficult problems for vision based navigation. In particular, the road markings are hard to see and traffic looks like a pattern of bright lights on a black background. Some of these problems can be addressed by developing systems which follow a human controlled lead vehicle. Although extracting the taillights of a lead vehicle is relatively straightforward, following cars which move at varying speeds on curved roads is a non-trivial problem. RACCOON is a car follower that has been implemented on the Carnegie Mellon Navlab II, a computer-controlled HMMWV testbed. The system successfully followed lead vehicles on winding roads at night in light traffic at 32 km/h.

Given the position of the lead vehicle, the straightforward approach to car following is to steer the autonomous vehicle so that it heads towards the taillights of the lead vehicle. Speed can be controlled so that the robot vehicle remains a constant distance behind the lead car. This naive implementation may produce satisfactory results on straight roads when both vehicles are moving at the same speed; however it fails in any realistic scenario since lead vehicles change speed and make turns to follow winding roads, and steering towards taillights results in corner cutting -- possibly causing an accident as the computer controlled vehicle drifts into oncoming traffic or off the road entirely. RACCOON solves these problems by creating an intermediate map structure which records the lead vehicle's trajectory. The path is represented by points in a global reference frame, and the computer controlled vehicle is steered from point to point. The autonomous vehicle follows this trail while keeping thelead vehicle's taillights in sight. Since every point on the trail is guaranteed to be on the road, the robot vehicle navigates around corners and obstacles rather than through them. A second important advantage is that the autonomous vehicle is not constrained to follow at a constant distance, but may instead follow at its own pace. By changing the problem from "car following" to "path tracking", the system is able to drive competently in real situations.