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Frontiers of Engineering Reports on Leading-Edge Engineering from the 2019 Symposium (2020) / Chapter Skim
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Influencing Interactions Between Human Drivers and Autonomous Vehicles - Dorsa Sadigh
Pages 83-90

The Chapter Skim interface presents what we've algorithmically identified as the most significant single chunk of text within every page in the chapter.
Select key terms on the right to highlight them within pages of the chapter.


From page 83...
... This is usually overlooked by the autonomous driving industry, where the common assumption is that humans act as external disturbances like moving obstacles, or that automation can always help societies without actually considering how humans may be impacted. Humans are not simply a disturbance to be avoided, and they do not always easily adapt to the proliferation of automation in their lives.
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From page 84...
... Leveraging optimization-based and game theoretic techniques, our work produces robot policies that influence human behavior toward safer outcomes in V2V interaction with autonomous cars (Sadigh et al.
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From page 85...
... . Planning for Interaction-Aware Controllers Once we have a predictive human driving model, we can plan for autonomous cars that better interact with humans by being "mindful" of how their actions influence humans.
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From page 86...
... demonstrate the trajectories from our users: orange represents the human trajectories where the autonomous car uses a model of the human, grey represents the human trajectories where the autonomous car does not use a model of the human, and purple represents the simulated learned human model. Figures (b–d)
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From page 87...
... We designed a pricing scheme for the AVs such that when autonomous service users choose from their options and human drivers selfishly choose their routes, road use is optimized and transit delay minimized. To do so, we formalized a model of how autonomous service users make choices between routes with different prices versus delay values.
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From page 88...
... We found that, in the absence of these policies, high demands and network perturbations result in large congestion, whereas using the policy greatly decreases travel times by minimizing congestion. SUMMARY We have described our work in planning for influencing interactions in autonomous driving at two levels: (i)
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From page 89...
... Accidents Accidents Autonomous cars Autonomous cars Human-driven cars Human-driven cars RL Policy # of Cars in the Queue # of Cars in Different Autonomous Traffic Network Parts of the Network Routing Queue Accident Information } FIGURE 2  Using deep reinforcement learning (RL) to dynamically route autonomous cars.
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From page 90...
... Proceedings, Robotics: Science and Systems, Jul 12–16, Cambridge MA. Sadigh D, Landolfi N, Sastry SS, Seshia SA, Dragan AD.
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Key Terms

  • autonomous cars
  • human drivers
  • autonomous vehicles
  • influencing interactions
  • reward function

  • reward functions
  • human model
  • changing lanes
  • autonomous service
  • human behavior

  • human driver
  • influence human
  • human trajectories
  • human driving
  • internatl conf

  • vehicle level
  • service users
  • theoretical results
  • parallel roads
  • routing decisions

  • fundamental diagram
  • traffic network
  • conf intelligent
  • routing choices
  • intelligent robots

  • driving behavior
  • learning reward
  • learned human
  • influences human
  • hence human


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