Systems and methods for global localization using multiple models and a map
AssigneeToyota Jidosha Kabushiki Kaisha
InventorAlexander Christoph Schaefer
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Abstract
Systems, methods, and other embodiments described herein relate to globally localizing a vehicle using transition and state probabilities associated with a standard map. In one embodiment, a method includes computing emission probabilities for a road position from sensor data and a map about a road. The method also includes estimating transition probabilities of the road position on a trellis graph from the sensor data and the map. The method also includes predicting state probabilities from the emission probabilities and the transition probabilities using a Markov model. The method also includes controlling a vehicle from vehicle locations using the state probabilities, and the vehicle locations being topological locations.
Claims20
A prediction system comprising:
- a memory storing instructions that, when executed by a processor, cause the processor to:
- compute emission probabilities for a road position from sensor data and a map about a road;
- estimate transition probabilities of the road position on a trellis graph from the sensor data and the map;
- predict state probabilities from the emission probabilities and the transition probabilities using a Markov model;
- and
- control a vehicle from vehicle locations using the state probabilities, and the vehicle locations being topological locations.
A non-transitory computer-readable medium comprising:
- instructions that when executed by a processor cause the processor to:
- compute emission probabilities for a road position from sensor data and a map about a road;
- estimate transition probabilities of the road position on a trellis graph from the sensor data and the map;
- predict state probabilities from the emission probabilities and the transition probabilities using a Markov model;
- and
- control a vehicle from vehicle locations using the state probabilities, and the vehicle locations being topological locations.
A method comprising:
- computing emission probabilities for a road position from sensor data and a map about a road;
- estimating transition probabilities of the road position on a trellis graph from the sensor data and the map;
- predicting state probabilities from the emission probabilities and the transition probabilities using a Markov model;
- and
- controlling a vehicle from vehicle locations using the state probabilities, and the vehicle locations being topological locations.
Dependencies are inferred from the claim text.