Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366342830> ?p ?o ?g. }
Showing items 1 to 69 of
69
with 100 items per page.
- W4366342830 abstract "In the field of motion simulation, the level of immersion strongly depends on the motion cueing algorithm (MCA), as it transfers the reference motion of the simulated vehicle to a motion of the motion simulation platform (MSP). The challenge for the MCA is to reproduce the motion perception of a real vehicle driver as accurately as possible without exceeding the limits of the workspace of the MSP in order to provide a realistic virtual driving experience. In case of a large discrepancy between the perceived motion signals and the optical cues, motion sickness may occur with the typical symptoms of nausea, dizziness, headache and fatigue. Existing approaches either produce non-optimal results, e.g., due to filtering, linearization, or simplifications, or the required computational time exceeds the real-time requirements of a closed-loop application. In this work a new solution is presented, where not a human designer specifies the principles of the MCA but an artificial intelligence (AI) learns the optimal motion by trial and error in an interaction with the MSP. To achieve this, deep reinforcement learning (RL) is applied, where an agent interacts with an environment formulated as a Markov decision process~(MDP). This allows the agent to directly control a simulated MSP to obtain feedback on its performance in terms of platform workspace usage and the motion acting on the simulator user. The RL algorithm used is proximal policy optimization (PPO), where the value function and the policy corresponding to the control strategy are learned and both are mapped in artificial neural networks (ANN). This approach is implemented in Python and the functionality is demonstrated by the practical example of pre-recorded lateral maneuvers. The subsequent validation on a standardized double lane change shows that the RL algorithm is able to learn the control strategy and improve the quality of..." @default.
- W4366342830 created "2023-04-20" @default.
- W4366342830 creator A5011193438 @default.
- W4366342830 creator A5015293969 @default.
- W4366342830 creator A5059050909 @default.
- W4366342830 creator A5064783025 @default.
- W4366342830 creator A5065056293 @default.
- W4366342830 creator A5072981510 @default.
- W4366342830 date "2023-04-15" @default.
- W4366342830 modified "2023-10-14" @default.
- W4366342830 title "A novel approach of a deep reinforcement learning based motion cueing algorithm for vehicle driving simulation" @default.
- W4366342830 doi "https://doi.org/10.48550/arxiv.2304.07600" @default.
- W4366342830 hasPublicationYear "2023" @default.
- W4366342830 type Work @default.
- W4366342830 citedByCount "0" @default.
- W4366342830 crossrefType "posted-content" @default.
- W4366342830 hasAuthorship W4366342830A5011193438 @default.
- W4366342830 hasAuthorship W4366342830A5015293969 @default.
- W4366342830 hasAuthorship W4366342830A5059050909 @default.
- W4366342830 hasAuthorship W4366342830A5064783025 @default.
- W4366342830 hasAuthorship W4366342830A5065056293 @default.
- W4366342830 hasAuthorship W4366342830A5072981510 @default.
- W4366342830 hasBestOaLocation W43663428301 @default.
- W4366342830 hasConcept C104114177 @default.
- W4366342830 hasConcept C105795698 @default.
- W4366342830 hasConcept C106189395 @default.
- W4366342830 hasConcept C11413529 @default.
- W4366342830 hasConcept C154945302 @default.
- W4366342830 hasConcept C159886148 @default.
- W4366342830 hasConcept C2775924081 @default.
- W4366342830 hasConcept C31972630 @default.
- W4366342830 hasConcept C33923547 @default.
- W4366342830 hasConcept C41008148 @default.
- W4366342830 hasConcept C44154836 @default.
- W4366342830 hasConcept C47446073 @default.
- W4366342830 hasConcept C58581272 @default.
- W4366342830 hasConcept C90509273 @default.
- W4366342830 hasConcept C97541855 @default.
- W4366342830 hasConceptScore W4366342830C104114177 @default.
- W4366342830 hasConceptScore W4366342830C105795698 @default.
- W4366342830 hasConceptScore W4366342830C106189395 @default.
- W4366342830 hasConceptScore W4366342830C11413529 @default.
- W4366342830 hasConceptScore W4366342830C154945302 @default.
- W4366342830 hasConceptScore W4366342830C159886148 @default.
- W4366342830 hasConceptScore W4366342830C2775924081 @default.
- W4366342830 hasConceptScore W4366342830C31972630 @default.
- W4366342830 hasConceptScore W4366342830C33923547 @default.
- W4366342830 hasConceptScore W4366342830C41008148 @default.
- W4366342830 hasConceptScore W4366342830C44154836 @default.
- W4366342830 hasConceptScore W4366342830C47446073 @default.
- W4366342830 hasConceptScore W4366342830C58581272 @default.
- W4366342830 hasConceptScore W4366342830C90509273 @default.
- W4366342830 hasConceptScore W4366342830C97541855 @default.
- W4366342830 hasLocation W43663428301 @default.
- W4366342830 hasOpenAccess W4366342830 @default.
- W4366342830 hasPrimaryLocation W43663428301 @default.
- W4366342830 hasRelatedWork W1910101490 @default.
- W4366342830 hasRelatedWork W2016398835 @default.
- W4366342830 hasRelatedWork W2029249305 @default.
- W4366342830 hasRelatedWork W2168146018 @default.
- W4366342830 hasRelatedWork W2511137960 @default.
- W4366342830 hasRelatedWork W2687972263 @default.
- W4366342830 hasRelatedWork W3192344083 @default.
- W4366342830 hasRelatedWork W3213838085 @default.
- W4366342830 hasRelatedWork W4313591620 @default.
- W4366342830 hasRelatedWork W4366330678 @default.
- W4366342830 isParatext "false" @default.
- W4366342830 isRetracted "false" @default.
- W4366342830 workType "article" @default.