Motion planning for autonomous driving: The state of the art and future perspectives
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …
convenience, safety advantages, and potential commercial value. Despite predictions of …
From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai
The rising popularity of explainable artificial intelligence (XAI) to understand high-performing
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
black boxes raised the question of how to evaluate explanations of machine learning (ML) …
Planning-oriented autonomous driving
Modern autonomous driving system is characterized as modular tasks in sequential order,
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
ie, perception, prediction, and planning. In order to perform a wide diversity of tasks and …
End-to-end autonomous driving: Challenges and frontiers
The autonomous driving community has witnessed a rapid growth in approaches that
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
embrace an end-to-end algorithm framework, utilizing raw sensor input to generate vehicle …
Gpt-driver: Learning to drive with gpt
We present a simple yet effective approach that can transform the OpenAI GPT-3.5 model
into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge …
into a reliable motion planner for autonomous vehicles. Motion planning is a core challenge …
Vad: Vectorized scene representation for efficient autonomous driving
Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …
Safety-enhanced autonomous driving using interpretable sensor fusion transformer
Large-scale deployment of autonomous vehicles has been continually delayed due to safety
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
concerns. On the one hand, comprehensive scene understanding is indispensable, a lack of …
Drivevlm: The convergence of autonomous driving and large vision-language models
A primary hurdle of autonomous driving in urban environments is understanding complex
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …
and long-tail scenarios, such as challenging road conditions and delicate human behaviors …
Wayformer: Motion forecasting via simple & efficient attention networks
Motion forecasting for autonomous driving is a challenging task because complex driving
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …
scenarios involve a heterogeneous mix of static and dynamic inputs. It is an open problem …
St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning
Many existing autonomous driving paradigms involve a multi-stage discrete pipeline of
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …
tasks. To better predict the control signals and enhance user safety, an end-to-end approach …