Interpretable and Flexible Target-Conditioned Neural Planners For Autonomous Vehicles

no code implementations • 23 Sep 2023 • Haolan Liu, Jishen Zhao, Liangjun Zhang

Learning-based approaches to autonomous vehicle planners have the potential to scale to many complicated real-world driving scenarios by leveraging huge amounts of driver demonstrations.

Autonomous Vehicles Collision Avoidance

Deep Learning Approaches in Pavement Distress Identification: A Review

no code implementations • 1 Aug 2023 • Sizhe Guan, Haolan Liu, Hamid R. Pourreza, Hamidreza Mahyar

This paper presents a comprehensive review of recent advancements in image processing and deep learning techniques for pavement distress detection and classification, a critical aspect in modern pavement management systems.

Management

Safety-Critical Scenario Generation Via Reinforcement Learning Based Editing

no code implementations • 25 Jun 2023 • Haolan Liu, Liangjun Zhang, Siva Kumar Sastry Hari, Jishen Zhao

Generating safety-critical scenarios is essential for testing and verifying the safety of autonomous vehicles.

Autonomous Vehicles reinforcement-learning

Coda: An End-to-End Neural Program Decompiler

no code implementations • NeurIPS 2019 • Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

Furthermore, Coda outperforms the sequence-to-sequence model with attention by a margin of 70% program accuracy.

Computer Security Malware Detection

A Neural-based Program Decompiler

no code implementations • 28 Jun 2019 • Cheng Fu, Huili Chen, Haolan Liu, Xinyun Chen, Yuandong Tian, Farinaz Koushanfar, Jishen Zhao

Reverse engineering of binary executables is a critical problem in the computer security domain.

Computer Security Malware Detection