Motion planning for autonomous driving: The state of the art and future perspectives

S Teng, X Hu, P Deng, B Li, Y Li, Y Ai… - IEEE Transactions …, 2023 - ieeexplore.ieee.org
Intelligent vehicles (IVs) have gained worldwide attention due to their increased
convenience, safety advantages, and potential commercial value. Despite predictions of …

From anecdotal evidence to quantitative evaluation methods: A systematic review on evaluating explainable ai

M Nauta, J Trienes, S Pathak, E Nguyen… - ACM Computing …, 2023 - dl.acm.org
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) …

Planning-oriented autonomous driving

Y Hu, J Yang, L Chen, K Li, C Sima… - Proceedings of the …, 2023 - openaccess.thecvf.com
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 …

End-to-end autonomous driving: Challenges and frontiers

L Chen, P Wu, K Chitta, B Jaeger… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
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 …

Gpt-driver: Learning to drive with gpt

J Mao, Y Qian, J Ye, H Zhao, Y Wang - arXiv preprint arXiv:2310.01415, 2023 - arxiv.org
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 …

Vad: Vectorized scene representation for efficient autonomous driving

B Jiang, S Chen, Q Xu, B Liao, J Chen… - Proceedings of the …, 2023 - openaccess.thecvf.com
Autonomous driving requires a comprehensive understanding of the surrounding
environment for reliable trajectory planning. Previous works rely on dense rasterized scene …

Safety-enhanced autonomous driving using interpretable sensor fusion transformer

H Shao, L Wang, R Chen, H Li… - Conference on Robot …, 2023 - proceedings.mlr.press
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 …

Drivevlm: The convergence of autonomous driving and large vision-language models

X Tian, J Gu, B Li, Y Liu, Y Wang, Z Zhao… - arXiv preprint arXiv …, 2024 - arxiv.org
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 …

Wayformer: Motion forecasting via simple & efficient attention networks

N Nayakanti, R Al-Rfou, A Zhou, K Goel… - … on Robotics and …, 2023 - ieeexplore.ieee.org
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 …

St-p3: End-to-end vision-based autonomous driving via spatial-temporal feature learning

S Hu, L Chen, P Wu, H Li, J Yan, D Tao - European Conference on …, 2022 - Springer
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 …