APEX: Autonomous Vehicle Plan Verification and Execution
Houssam Abbas, Matthew O'Kelly, Sicun Gao, Shin'ichi Shiraishi, Shinpei Kato, Rahul Mangharam

Citation
Houssam Abbas, Matthew O'Kelly, Sicun Gao, Shin'ichi Shiraishi, Shinpei Kato, Rahul Mangharam. "APEX: Autonomous Vehicle Plan Verification and Execution". submitted to the Society of Automotive Engineers, 2015.

Abstract
Autonomous vehicles (AVs) have already driven millions of miles on public roads, but even the simplest scenarios have not been certified for safety. Current methodologies for the verification of AV's decision and control systems attempt to divorce the lower level, short-term trajectory planning and trajectory tracking functions from the behavioral rules-based framework that governs mid-term actions. Such analysis is typically predicated on the discretization of the state space and has several limitations. First, it requires that a conservative buffer be added around obstacles such that many feasible plans are classified as unsafe. Second, the discretized controllers modeled in this analysis require several refinement steps before being implementable on an actual AV, and typically do not allow the specification of comfort-related properties on the trajectories. In contrast, consumer-ready AVs use motion planning algorithms that generate smooth trajectories. While viable algorithms exist for the generation of smooth trajectories originating from a single state, analysis should consider that the AV faces state estimation errors and disturbances. Third, verification is restricted to a discretized state space with fixed-size cells; this assumption can artificially limit the set of available trajectories if the discretization is too coarse. Conversely, too fine of a discretization renders the problem intractable for automated analysis. This work presents a new verification tool, APEX, which investigates the combined action of a behavioral planner and state lattice-based motion planner to guarantee a safe vehicle trajectory is chosen. In APEX, decisions made at the behavioral layer can be traced through to the spatio-temporal evolution of the AV and verified. Thus, there is no need to create abstractions of the AV's controllers, and aggressive trajectories required for evasive maneuvers can be accurately investigated.

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Citation formats

• HTML

  Houssam Abbas, Matthew O'Kelly, Sicun Gao, Shin'ichi
  Shiraishi, Shinpei Kato, Rahul Mangharam. <a
  href="http://www.terraswarm.org/pubs/704.html"
  >APEX: Autonomous Vehicle Plan Verification and
  Execution</a>, <i>submitted to the Society of
  Automotive Engineers</i>,  2015.

• Plain text

  Houssam Abbas, Matthew O'Kelly, Sicun Gao, Shin'ichi
  Shiraishi, Shinpei Kato, Rahul Mangharam. "APEX:
  Autonomous Vehicle Plan Verification and Execution".
  <i>submitted to the Society of Automotive
  Engineers</i>,  2015.

• BibTeX

  @article{AbbasOKellyGaoShiraishiKatoMangharam15_APEXAutonomousVehiclePlanVerificationExecution,
      author = {Houssam Abbas and Matthew O'Kelly and Sicun Gao
                and Shin'ichi Shiraishi and Shinpei Kato and Rahul
                Mangharam},
      title = {APEX: Autonomous Vehicle Plan Verification and
                Execution},
      journal = {submitted to the Society of Automotive Engineers},
      year = {2015},
      abstract = {Autonomous vehicles (AVs) have already driven
                millions of miles on public roads, but even the
                simplest scenarios have not been certified for
                safety. Current methodologies for the verification
                of AV's decision and control systems attempt to
                divorce the lower level, short-term trajectory
                planning and trajectory tracking functions from
                the behavioral rules-based framework that governs
                mid-term actions. Such analysis is typically
                predicated on the discretization of the state
                space and has several limitations. First, it
                requires that a conservative buffer be added
                around obstacles such that many feasible plans are
                classified as unsafe. Second, the discretized
                controllers modeled in this analysis require
                several refinement steps before being
                implementable on an actual AV, and typically do
                not allow the specification of comfort-related
                properties on the trajectories. In contrast,
                consumer-ready AVs use motion planning algorithms
                that generate smooth trajectories. While viable
                algorithms exist for the generation of smooth
                trajectories originating from a single state,
                analysis should consider that the AV faces state
                estimation errors and disturbances. Third,
                verification is restricted to a discretized state
                space with fixed-size cells; this assumption can
                artificially limit the set of available
                trajectories if the discretization is too coarse.
                Conversely, too fine of a discretization renders
                the problem intractable for automated analysis.
                This work presents a new verification tool, APEX,
                which investigates the combined action of a
                behavioral planner and state lattice-based motion
                planner to guarantee a safe vehicle trajectory is
                chosen. In APEX, decisions made at the behavioral
                layer can be traced through to the spatio-temporal
                evolution of the AV and verified. Thus, there is
                no need to create abstractions of the AV's
                controllers, and aggressive trajectories required
                for evasive maneuvers can be accurately
                investigated.},
      URL = {http://terraswarm.org/pubs/704.html}
  }
  

Posted by Elizabeth Coyne on 1 Dec 2015.
Groups: tools