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

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 vehicle driving off-road through challenging desert terrain. The vehicle should drive slowly on terrain that poses substantial risk. However, it should not dawdle 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 realtime and without human intervention. The trade-off is optimized using supervised learning to match human 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 hundreds of miles of autonomous driving, including performance during the 2005 DARPA Grand Challenge. This approach was the deciding factor in our vehicle’s speed for nearly 20% of the DARPA competition – more than any other constraint except the DARPA-imposed speed limits – and resulted in the fastest finishing time.