Fu et al., 2024 - Google Patents
Limsim++: A closed-loop platform for deploying multimodal llms in autonomous drivingFu et al., 2024
View PDF- Document ID
- 16038911643828162086
- Author
- Fu D
- Lei W
- Wen L
- Cai P
- Mao S
- Dou M
- Shi B
- Qiao Y
- Publication year
- Publication venue
- 2024 IEEE Intelligent Vehicles Symposium (IV)
External Links
Snippet
The emergence of Multimodal Large Language Models ((M) LLMs) has ushered in new avenues in artificial intelligence, particularly for autonomous driving by offering enhanced understanding and reasoning capabilities. This paper introduces LimSim++, an extended …
- 238000004088 simulation 0 abstract description 25
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N5/00—Computer systems utilising knowledge based models
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Fu et al. | Limsim++: A closed-loop platform for deploying multimodal llms in autonomous driving | |
| Chen et al. | Interpretable end-to-end urban autonomous driving with latent deep reinforcement learning | |
| Chen et al. | End-to-end autonomous driving: Challenges and frontiers | |
| Xu et al. | Bits: Bi-level imitation for traffic simulation | |
| Stepputtis et al. | Language-conditioned imitation learning for robot manipulation tasks | |
| EP4150426B1 (en) | Tools for performance testing and/or training autonomous vehicle planners | |
| Zheng et al. | Planagent: A multi-modal large language agent for closed-loop vehicle motion planning | |
| Takács et al. | Highly automated vehicles and self-driving cars [industry tutorial] | |
| Mei et al. | Continuously learning, adapting, and improving: A dual-process approach to autonomous driving | |
| Hu et al. | A survey of decision-making and planning methods for self-driving vehicles | |
| Paniego et al. | Autonomous driving in traffic with end-to-end vision-based deep learning | |
| Tian et al. | Large (vision) language models for autonomous vehicles: Current trends and future directions | |
| Sheng et al. | Curricuvlm: Towards safe autonomous driving via personalized safety-critical curriculum learning with vision-language models | |
| Chen et al. | Motion planning using feasible and smooth tree for autonomous driving | |
| Gao et al. | From words to collisions: Llm-guided evaluation and adversarial generation of safety-critical driving scenarios | |
| Kou et al. | Padriver: Towards personalized autonomous driving | |
| Carruth | Simulation for training and testing intelligent systems | |
| Yang et al. | Suicidal pedestrian: Generation of safety-critical scenarios for autonomous vehicles | |
| Arbabi et al. | Planning for autonomous driving via interaction-aware probabilistic action policies | |
| Xin et al. | Litsim: A conflict-aware policy for long-term interactive traffic simulation | |
| Zhang et al. | Bench2ADVLM: a closed-loop benchmark for vision-language models in autonomous driving | |
| Fu et al. | LimSim Series: An Autonomous Driving Simulation Platform for Validation and Enhancement | |
| Gao et al. | Laser: Script execution by autonomous agents for on-demand traffic simulation | |
| Gao et al. | NuRisk: A Visual Question Answering Dataset for Agent-Level Risk Assessment in Autonomous Driving | |
| Zhu et al. | Curriculum Reinforcement Learning for Autonomous Planning in Unprotected Left Turn Scenarios |