Safe Motion Planning for Autonomous Driving

@inproceedings{Wylde2012SafeMP,
  title={Safe Motion Planning for Autonomous Driving},
  author={Micah Wylde},
  year={2012}
}
Self-driving cars have the potential to revolutionize transportation by making it cheaper, safer, and more efficient. In this thesis we describe a novel motion planning system, which translates high-level navigation goals into low-level actions for controlling a vehicle. Specifically, the motion planning system is responsible for choosing at each time step an appropriate velocity and steering angle, which can then be implemented by the driving hardware or simulator. Our planner is able to… 

References

SHOWING 1-10 OF 26 REFERENCES
Motion planning in urban environments
TLDR
This approach combines a model-predictive trajectory generation algorithm for computing dynamically feasible actions with two higher level planners for generating long-range plans in both on-road and unstructured areas of the environment.
Smooth trajectory planning for fully automated passengers vehicles - spline and clothoid based methods and its simulation
TLDR
This chapter addresses the problem of fully automated operation of trajectory planning methods by proposing an approach that consists of introducing a velocity planning stage to generate adequate time sequences for usage in the interpolating curve planners.
Avoiding cars and pedestrians using velocity obstacles and motion prediction
TLDR
An iterative planning approach that addresses the problem of estimating the future behaviour of moving obstacles and to use the resulting estimates in trajectory computation and an iterative motion planning technique based on the concept of Velocity Obstacles.
Optimal and Efficient Path Planning for Unknown and Dynamic Environments
TLDR
The task of planning trajectories for a mobile robot has received considerable attention in the research literature, but less attention has been paid to the problem of unknown or partially-known environments.
Path following for autonomous vehicle navigation with inherent safety and dynamics margin
This paper addresses the path following problem for autonomous Ackermann-like vehicle navigation. A control strategy that takes into account both kinodynamic and configuration space constraints of
Planning Long Dynamically-Feasible Maneuvers for Autonomous Vehicles
TLDR
An algorithm for generating complex dynamically-feasible maneuvers for autonomous vehicles traveling at high speeds over large distances based on performing anytime incremental search on a multiresolution, dynamically- Feasible lattice state space is presented.
Detection, prediction, and avoidance of dynamic obstacles in urban environments
TLDR
After detecting a dynamic obstacle, the approach exploits structure in the environment where possible to generate a set of likely hypotheses for the future behavior of the obstacle and efficiently incorporates these hypotheses into the planning process to produce safe actions.
Incremental Learning for Motion Prediction of Pedestrians and Vehicles
TLDR
This thesis proposes a novel extension to Hidden Markov Models which allows simultaneous learning and utilization of the model, and incrementally builds a topological map – representing the model's structure – and reestimates the model’s parameters.
Object Detection and Tracking for Autonomous Navigation in Dynamic Environments
TLDR
This work addresses the problem of vision-based navigation in busy inner-city locations, using a stereo rig mounted on a mobile platform that combines classical geometric world mapping with object category detection and tracking and recovers the objects’ trajectories.
Anytime Dynamic A*: An Anytime, Replanning Algorithm
TLDR
A graph-based planning and replanning algorithm able to produce bounded suboptimal solutions in an anytime fashion that combines the benefits of anytime and incremental planners to provide efficient solutions to complex, dynamic search problems.
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