Terrestrial Domain
Advancing the future of transportation and robotics
Terrestrial Domain Projects
Our terrestrial domain research explores autonomous technologies for driverless cars, ground-based vehicles, and robotic manipulators.
Deep Learning based Stereo Depth Completion and Guidance for Autonomous Vehicles
This project aims to develop deep-learning-based autonomous driving in urban scenarios. To achieve this objective, the self-driving car must maintain high robustness when facing challenges from dynamic traffic conditions and adverse weather.
The proposed solution comprises a real-time perception system and a novel guidance and decision-making model. To enhance understanding of the surrounding environment, stereo-based depth estimation and semantic segmentation techniques are employed to capture scene geometry and semantic information. These outputs from the perception system are then fed into a Deep Reinforcement Learning (DRL) based guidance model, which generates driving commands to control the vehicle.
Robust Guidance Decision-Making for Single-Agent Autonomous Vehicles
This work addresses the vulnerability of Deep Reinforcement Learning (DRL) based single-agent guidance decision-making for autonomous vehicles to adversarial perception attacks.
An efficient gradient-based method is proposed to generate adversarial perturbations and a saliency-based detection network to flag attacks on sensor inputs. To ensure safe guidance under such perturbations, a robust DRL framework is developed using PPO with a theoretically grounded, multi-objective constrained optimisation strategy. Evaluations in complex roundabout scenarios show the approach significantly improves resilience and driving safety under adversarial conditions.
Monocular 3D Object Detection for Autonomous Vehicles
Monocular 3D object detection is challenging for autonomous driving due to limited depth information. This work proposes a novel approach that enhances deep networks with depth cues to improve spatial understanding from single images.
The method includes: A Feature Enhancement Pyramid Module to fuse multi-scale features and improve contextual awareness. An Auxiliary Dense Depth Estimator to enrich spatial perception without added computational cost. An Augmented Centre Depth Regression using geometric cues. Experiments demonstrate real-time and accurate performance, offering a promising monocular 3D object detection solution for enhancing autonomous driving perception.
Robust Guidance Decision-Making for Multi-Agent Autonomous Vehicles
While Multi-Agent Reinforcement Learning (MARL) significantly enhances coordination and system stability compared to single-agent approaches, it also introduces increased vulnerability to adversarial perturbations. These attacks on observation inputs can mislead one or more agents, resulting in unsafe behaviours and potential multi-vehicle collisions.
To address this, Robust Constrained Cooperative Multi-Agent Reinforcement Learning (R-CCMARL) is proposed, which employs a universal policy shared across agents and leverages Mean-Field modelling to effectively manage dynamic multi-agent interactions. A risk estimation network is incorporated to assess long-term safety and inform a constrained optimisation objective that balances robustness and task performance, even under adversarial conditions. Extensive experiments in CARLA intersection scenarios demonstrate that R-CCMARL maintains high performance while significantly improving resilience against observation-based attacks.