Safety and Efficiency in Autonomous Vehicles through Online Learning

Zachary Sunberg

Postdoctoral Scholar

University of California

Map with Contributions

Map with Autorotation

Autorotation

"Expert System" Autorotation Controller

Example Controller: Flare

 

\[\alpha \equiv \frac{KE_{\text{available}} - KE_{\text{flare exit}}}{KE_{\text{flare entry}} - KE_{\text{flare exit}}}\]

\[TTLE = TTLE_{max} \times \alpha\]

\[\ddot{h}_{des} = -\frac{2}{TTI_F^2}h - \frac{2}{TTI_F}\dot{h}\]

Problems with this approach:

  • No optimality justification
  • Controller must be adjusted by hand to accommodate any changes
  • Difficult to communicate about
  • Parameters not intuitive

Autorotation Simulation Results

Autonomy

Two Objectives for Autonomy

EFFICIENCY

SAFETY

Minimize resource use

(especially time)

Minimize the risk of harm to oneself and others

Hard Safety: Guaranteeing that there will be no harm

Soft Safety: Reducing the risk of harm

Proper Uncertainty Modeling Can Improve Both

Tweet by Nitin Gupta

29 April 2018

https://twitter.com/nitguptaa/status/990683818825736192

Sadigh, Dorsa, et al. "Information gathering actions over human internal state." Intelligent Robots and Systems (IROS), 2016 IEEE/RSJ International Conference on. IEEE, 2016.

Schmerling, Edward, et al. "Multimodal Probabilistic Model-Based Planning for Human-Robot Interaction." arXiv preprint arXiv:1710.09483 (2017).

Sadigh, Dorsa, et al. "Planning for Autonomous Cars that Leverage Effects on Human Actions." Robotics: Science and Systems. 2016.

Human Behavior Model: IDM and MOBIL

\ddot{x}_\text{IDM} = a \left[ 1 - \left( \frac{\dot{x}}{\dot{x}_0} \right)^{\delta} - \left(\frac{g^*(\dot{x}, \Delta \dot{x})}{g}\right)^2 \right]
x¨IDM=a[1(x˙x˙0)δ(g(x˙,Δx˙)g)2] \ddot{x}_\text{IDM} = a \left[ 1 - \left( \frac{\dot{x}}{\dot{x}_0} \right)^{\delta} - \left(\frac{g^*(\dot{x}, \Delta \dot{x})}{g}\right)^2 \right]
g^*(\dot{x}, \Delta \dot{x}) = g_0 + T \dot{x} + \frac{\dot{x}\Delta \dot{x}}{2 \sqrt{a b}}
g(x˙,Δx˙)=g0+Tx˙+x˙Δx˙2abg^*(\dot{x}, \Delta \dot{x}) = g_0 + T \dot{x} + \frac{\dot{x}\Delta \dot{x}}{2 \sqrt{a b}}

M. Treiber, et al., “Congested traffic states in empirical observations and microscopic simulations,” Physical Review E, vol. 62, no. 2 (2000).

A. Kesting, et al., “General lane-changing model MOBIL for car-following models,” Transportation Research Record, vol. 1999 (2007).

A. Kesting, et al., "Agents for Traffic Simulation." Multi-Agent Systems: Simulation and Applications. CRC Press (2009).

Objective: Safely Change 3 Lanes within 1000m

All drivers normal

Omniscient

Simulation results

All drivers normal

MDP

Omniscient

Mean MPC

QMDP

POMCPOW (Ours)

Simulation results

Monte Carlo Tree Search

Image by Dicksonlaw583 (CC 4.0)

All drivers normal

MDP

Omniscient

Mean MPC

QMDP

POMCPOW (Ours)

Simulation results

All drivers normal

MDP

Omniscient

Mean MPC

QMDP

POMCPOW (Ours)

Simulation results

Conditional Parameter Distributions

Assume normal

Outcome only

Omniscient

Mean MPC

QMDP

POMCPOW

MAP

References

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Acknowledgements

The content of my research reflects my opinions and conclusions, and is not necessarily endorsed by my funding organizations.

TODO: HSL PICTURE

Thank You!

Job Talk II

By Zachary Sunberg

Job Talk II

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