Conference Paper

Online Speed Adaptation Using Supervised Learning for High-Speed, Off-Road Autonomous Driving.

Conference: IJCAI 2007, Proceedings of the 20th International Joint Conference on Artificial Intelligence, Hyderabad, India, January 6-12, 2007
Source: DBLP

ABSTRACT The mobile robotics community has traditionally addressed motion planning and navigation in terms of steering decisions. However, selecting the best speed is also important - beyond its relationship to stopping distance and lateral maneuverability. Consider a high-speed (35 mph) autonomous vehi- cle driving off-road through challenging desert ter- rain. The vehicle should drive slowly on terrain that poses substantial risk. However, it should not daw- dle on safe terrain. In this paper we address one aspect of risk - shock to the vehicle. We present an algorithm for trading-off shock and speed in real- time and without human intervention. The trade-off is optimized using supervised learning to match hu- man driving. The learning process is essential due to the discontinuous and spatially correlated nature of the control problem - classical techniques do not directly apply. We evaluate performance over hun- dreds of miles of autonomous driving, including performance during the 2005 DARPA Grand Chal- lenge. This approach was the deciding factor in our vehicle's speed for nearly 20% of the DARPA com- petition - more than any other constraint except the DARPA-imposed speed limits - and resulted in the fastest finishing time. In mobile robotics, motion planning and navigation have tra- ditionally focused on steering decisions. This paper presents speed decisions as another crucial part of planning - beyond the relationship of speed to obstacle avoidance concerns, such as stopping distance and lateral maneuverability. Consider a high-speed (35 mph) autonomous vehicle driving off-road through challenging desert terrain. We want the vehicle to drive slower on more dangerous terrain. However, we also want to minimize completion time. Thus, the robot must trade-off speed and risk in real-time. This is a natural pro- cess for human drivers, but it is not at all trivial to endow a robot with this ability. We address this trade-off for one component of risk: the shock the vehicle experiences. Minimizing shock is impor- tant for several reasons. First, shock increases the risk of damage to the vehicle, its mechanical actuators, and its elec- tronic components. Second, a key perceptive technology, laser range scanning, relies on accurate estimation of orien- tation. Shock causes the vehicle to shake violently, making accurate estimates difficult. Third, shocks substantially re- duce traction during oscillations. Finally, we demonstrate that shock is strongly correlated with speed and, independently, with subjectively difficult terrain. That is, minimizing shock implies slowing on challenging roads when necessary - a cru- cial behavior to mitigate risk to the vehicle. Our algorithm uses the linear relationship between shock and speed which we derive analytically. The algorithm has three states. First, the vehicle drives at the maximum allowed speed until a shock threshold is exceeded. Second, the vehicle slows immediately to bring itself within the shock threshold using the relationship between speed and shock. Finally, the

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