Revolutionizing Autonomous Driving with Embodied AI
Table of Contents
- Introduction
- The Complexity of Autonomous Driving
- Embodied AI for Autonomous Driving
- AV 2.0 and Adaptive Driving Intelligence
- Generalization Across Cities and Countries
- Vehicle and Sensor Agnostic Approach
- Aligning with Human Driving
- Embodied Intelligence in AI
- Breakthroughs in AI
- Large Training Data Sets and Benchmarks
- Games and Simulation
- Self-Supervised Learning
- Foundation Models
- Scalable Compute
- Scalable Architectures (e.g., Transformers)
- Multimodality in AI
- Human Feedback and Alignment
- Simulation for Evaluation
- Simulating Autonomous Driving
- Benefits of Simulation
- Wave Infinity Simulator
- Domain Gaps and Neural Radiance Fields
- Closing the Gap between Simulation and Reality
- Reinforcement Learning for Driving
- Overview of Reinforcement Learning
- Autonomous Driving with RL
- Challenges of RL in the Real World
- Multi-Agent RL and Dense Environments
- Universal Agents
- Evaluating Driving Performance
- Language Meets Driving
- Advances in Language Models
- Encoding Knowledge about Driving
- Grounding Language and Vision
- Dataset for Vision and Language Grounding
- Learning Rules of the Road from Text
- Using Intervention Reports for Supervision
- Exploiting Reasoning Capabilities
- World Models
- Predicting Future in Autonomous Driving
- World Models in Driving
- Supervised and Self-supervised Approaches
- Auto-regressive Prediction of Frames
- Real Data without Supervision
- Controlling World Models
- Integration into On-road Driving
- Conclusion
Embodied AI for Autonomous Driving
Autonomous driving presents complex challenges due to the diverse and dynamic nature of urban streets. Wave, a pioneer in embodied AI for autonomous driving, approaches this problem by adopting the AV 2.0 framework. They focus on developing adaptive and scalable driving intelligence that generalizes to new cities, countries, and driving cultures. This driving intelligence is designed to be vehicle and sensor agnostic while aligning with human driving behavior. By leveraging the power of their AI models, Wave has achieved promising results in real-world scenarios, such as driving across different cities in the UK and partnering with companies like Ocado and Asda for autonomous grocery deliveries.
Introduction
In recent years, the field of AI has witnessed groundbreaking advancements in various domains. From solving complex image recognition tasks to mastering intricate games like chess and Go, AI has reached new heights of capability. These advancements can be attributed to several factors, including large training data sets, benchmarks, games and simulation, self-supervised learning, foundation models, scalable compute, scalable architectures (such as Transformers), multimodality, and the ability to leverage human feedback to Align with human goals and preferences. As the field of AI continues to evolve, the next major inflection point is believed to be embodied intelligence.
The Complexity of Autonomous Driving
Autonomous driving is a challenging task that requires a deep understanding of the complex and unpredictable nature of urban streets. The streets of cities like London present a myriad of scenarios, including buses undertaking motor mopeds, bicycles zooming across lanes, multi-lane roundabouts, and even buses going down the wrong way. These examples highlight the enormity of the complexity involved in autonomous driving. Wave recognizes this complexity and approaches autonomous driving as an embodied AI problem.
Embodied AI for Autonomous Driving
Wave has developed the AV 2.0 framework, which focuses on adaptive and scalable driving intelligence for autonomous vehicles. Their driving intelligence is designed to generalize across different cities, countries, and driving cultures, making it versatile and adaptable. Moreover, Wave's approach is vehicle and sensor agnostic, allowing their models to work seamlessly with a variety of vehicle platforms. By aligning their driving policies with human driving behavior, Wave demonstrates the power of their approach through real-world applications like autonomous deliveries with partners like Ocado and Asda.
Embodied Intelligence in AI
The recent advancements in AI can largely be attributed to key breakthroughs in the field. These breakthroughs have been driven by factors such as large training data sets, benchmarks, games and simulation, self-supervised learning, foundation models, scalable compute, scalable architectures, multimodality, and the ability to leverage human feedback to align with human goals and preferences. These factors have paved the way for embodied intelligence, which is poised to be the next major inflection point in AI.
Simulation for Evaluation
Simulation plays a crucial role in evaluating and testing autonomous driving systems. While real-world testing is essential, it is expensive, slow, and potentially dangerous. Simulation provides a cost-effective, repeatable, and scalable alternative that allows for rapid iteration and exploration of different scenarios. Wave has developed the Wave Infinity Simulator, a powerful tool that enables the generation of diverse and realistic driving scenarios. By bridging the gap between simulation and reality, Wave aims to improve the safety and performance of their driving models.
Reinforcement Learning for Driving
Reinforcement learning (RL) offers a promising approach for training autonomous driving models. RL allows agents to learn from interactions with the environment, receiving rewards or penalties Based on their actions. Wave has been exploring RL for driving, starting with early experiments on country roads and gradually scaling up to densely populated urban environments. They have achieved impressive results, demonstrating the ability to learn complex driving behaviors and navigate busy intersections. However, challenges remain, such as data inefficiency, exploration, and the need to bridge the gap between simulation and the real world.
Language Meets Driving
The emergence of large language models has opened up new possibilities for integrating language understanding with driving intelligence. These models, such as GPT, encode a vast amount of knowledge about driving, enabling them to answer questions and understand the implications of different driving scenarios. Wave is exploring the intersection of language and driving by grounding language in vision and action. They aim to develop models that can understand and reason about driving instructions, rules, and complex situations. By combining language understanding with driving intelligence, Wave seeks to enhance the capabilities of autonomous vehicles.
World Models
World models play a crucial role in autonomous driving by predicting the future evolution of driving scenes. They enable agents to anticipate different possibilities and make informed decisions. Wave has been actively researching world models, both in simulation and real-world scenarios. They have developed models that can predict diverse and plausible futures for up to an hour ahead. These models have been trained using supervised and self-supervised approaches, leveraging techniques such as auto-regressive prediction. The challenge lies in controlling and integrating these world models into on-road driving policies.
Conclusion
Embodied AI for autonomous driving represents a significant opportunity for advancements in the field. Wave's approach to adaptive and scalable driving intelligence, combined with simulation, reinforcement learning, language understanding, and world modeling, showcases the potential of embodied AI. As the complexity and demands of autonomous driving Continue to grow, there are various challenges that need to be addressed. However, with continued research and development, Wave aims to push the boundaries of what is possible in the realm of autonomous driving and Shape the future of transportation.
