Publications

2022

1. 

   DQ-GAT: Towards Safe and Efficient Autonomous Driving with Deep Q-Learning and Graph Attention Networks

   Peide Cai, Hengli Wang, Yuxiang Sun, and Ming Liu

   IEEE Transactions on Intelligent Transportation Systems (T-ITS) , 2022

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   Autonomous driving in multi-agent dynamic traffic scenarios is challenging: the behaviors of road users are uncertain and are hard to model explicitly, and the ego-vehicle should apply complicated negotiation skills with them, such as yielding, merging and taking turns, to achieve both safe and efficient driving in various settings. Traditional planning methods are largely rule-based and scale poorly in these complex dynamic scenarios, often leading to reactive or even overly conservative behaviors. Therefore, they require tedious human efforts to maintain workability. Recently, deep learning-based methods have shown promising results with better generalization capability but less hand engineering efforts. However, they are either implemented with supervised imitation learning (IL), which suffers from dataset bias and distribution mismatch issues, or are trained with deep reinforcement learning (DRL) but focus on one specific traffic scenario. In this work, we propose DQ-GAT to achieve scalable and proactive autonomous driving, where graph attention-based networks are used to implicitly model interactions, and deep Q-learning is employed to train the network end-to-end in an unsupervised manner. Extensive experiments in a high-fidelity driving simulator show that our method achieves higher success rates than previous learning-based methods and a traditional rule-based method, and better trades off safety and efficiency in both seen and unseen scenarios. Moreover, qualitative results on a trajectory dataset indicate that our learned policy can be transferred to the real world for practical applications with real-time speeds.

2021

1. 

   Carl-Lead: Lidar-based End-to-End Autonomous Driving with Contrastive Deep Reinforcement Learning

   Peide Cai, Sukai Wang, Hengli Wang, and Ming Liu

   preprint , 2021

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   Autonomous driving in urban crowds at unregulated intersections is challenging, where dynamic occlusions and uncertain behaviors of other vehicles should be carefully considered. Traditional methods are heuristic and based on hand-engineered rules and parameters, but scale poorly in new situations. Therefore, they require high labor cost to design and maintain rules in all foreseeable scenarios. Recently, deep reinforcement learning (DRL) has shown promising results in urban driving scenarios. However, DRL is known to be sample inefficient, and most previous works assume perfect observations such as ground-truth locations and motions of vehicles without considering noises and occlusions, which might be a too strong assumption for policy deployment. In this work, we use DRL to train lidar-based end-to-end driving policies that naturally consider imperfect partial observations. We further use unsupervised contrastive representation learning as an auxiliary task to improve the sample efficiency. The comparative evaluation results reveal that our method achieves higher success rates than the state-of-the-art (SOTA) lidar-based end-to-end driving network, better trades off safety and efficiency than the carefully tuned rule-based method, and generalizes better to new scenarios than the baselines.

2. 

   Vision-Based Autonomous Car Racing Using Deep Imitative Reinforcement Learning

   Peide Cai, Hengli Wang, Huaiyang Huang, Yuxuan Liu, and Ming Liu

   IEEE Robotics and Automation Letters (RA-L) &
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​) , 2021

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   Autonomous car racing is a challenging task in the robotic control area. Traditional modular methods require accurate mapping, localization and planning, which makes them computationally inefficient and sensitive to environmental changes. Recently, deep-learning-based end-to-end systems have shown promising results for autonomous driving/racing. However, they are commonly implemented by supervised imitation learning (IL), which suffers from the distribution mismatch problem, or by reinforcement learning (RL), which requires a huge amount of risky interaction data. In this work, we present a general deep imitative reinforcement learning approach (DIRL), which successfully achieves agile autonomous racing using visual inputs. The driving knowledge is acquired from both IL and model-based RL, where the agent can learn from human teachers as well as perform self-improvement by safely interacting with an offline world model. We validate our algorithm both in a high-fidelity driving simulation and on a real-world 1/20-scale RC-car with limited onboard computation. The evaluation results demonstrate that our method outperforms previous IL and RL methods in terms of sample efficiency and task performance.

3. 

   DiGNet: Learning Scalable Self-Driving Policies for Generic Traffic Scenarios with Graph Neural Networks

   Peide Cai, Hengli Wang, Yuxiang Sun, and Ming Liu

   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​) , 2021

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   Traditional modular self-driving frameworks scale poorly in new scenarios, which usually require tedious hand-tuning of rules and parameters to maintain acceptable performance in all foreseeable occasions. Therefore, robust and safe self-driving using traditional frameworks is still challenging, especially in complex and dynamic environments. Recently, deep-learning based self-driving methods have shown promising results with better generalization capability but less hand engineering effort. However, most of the previous learning-based methods are trained and evaluated in limited driving scenarios with scattered tasks, such as lane-following, autonomous braking, and conditional driving. In this paper, we propose a graph-based deep network to achieve scalable self-driving that can handle massive traffic scenarios. Specifically, more than 7,000 km of evaluation is conducted in a high-fidelity driving simulator, in which our method can obey the traffic rules and safely navigate the vehicle in a large variety of urban, rural, and highway environments, including unprotected left turns, narrow roads, roundabouts, and pedestrian-rich intersections. The results also show that our method achieves better performance over the baselines in terms of success rate.

4. 

   Learning interpretable end-to-end vision-based motion planning for autonomous driving with optical flow distillation

   Hengli Wang, Peide Cai, Yuxiang Sun, Lujia Wang, and Ming Liu

   In IEEE International Conference on Robotics and Automation (ICRA) , 2021

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5. 

   PVStereo: Pyramid voting module for end-to-end self-supervised stereo matching

   Hengli Wang*, Rui Fan*, Peide Cai, and Ming Liu

   IEEE Robotics and Automation Letters (RA-L) &
   IEEE International Conference on Robotics and Automation (ICRA) , 2021

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6. 

   Learning collision-free space detection from stereo images: Homography matrix brings better data augmentation

   Rui Fan*, Hengli Wang*, Peide Cai, Jin Wu, Mohammud Junaid Bocus, Lei Qiao, and Ming Liu

   IEEE/ASME Transactions on Mechatronics , 2021

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7. 

   The role of the hercules autonomous vehicle during the COVID-19 pandemic: An autonomous logistic vehicle for contactless goods transportation

   Tianyu Liu, Qinghai Liao, Lu Gan, etc., Peide Cai, Yuxiang Sun, Yandong Liu, Lujia Wang, and Ming Liu

   IEEE Robotics and Automation Magazine &
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021

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8. 

   SNE-RoadSeg+: Rethinking depth-normal translation and deep supervision for freespace detection

   Hengli Wang*, Rui Fan*, Peide Cai, and Ming Liu

   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2021

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9. 

   End-to-end interactive prediction and planning with optical flow distillation for autonomous driving

   Hengli Wang, Peide Cai, Rui Fan, Yuxiang Sun, and Ming Liu

   In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) , 2021

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10. 

    PointMoSeg: Sparse Tensor-Based End-to-End Moving-Obstacle Segmentation in 3-D Lidar Point Clouds for Autonomous Driving

    Yuxiang Sun, Weixun Zuo, Huaiyang Huang, Peide Cai, and Ming Liu

    IEEE Robotics and Automation Letters (RA-L) &
    IEEE International Conference on Robotics and Automation (ICRA) , 2021

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11. 

    DiTNet: End-to-End 3D Object Detection and Track ID Assignment in Spatio-Temporal World

    Sukai Wang, Peide Cai, Lujia Wang, and Ming Liu

    IEEE Robotics and Automation Letters (RA-L) &
    IEEE International Conference on Robotics and Automation (ICRA) , 2021

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2020

1. 

   VTGNet: A Vision-based Trajectory Generation Network for Autonomous Vehicles in Urban Environments

   Peide Cai, Yuxiang Sun, Hengli Wang, and Ming Liu

   IEEE Transactions on Intelligent Vehicles (T-IV) , 2020

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   Traditional methods for autonomous driving are implemented with many building blocks from perception, planning and control, making them difficult to generalize to varied scenarios due to complex assumptions and interdependencies. Recently, the end-to-end driving method has emerged, which performs well and generalizes to new environments by directly learning from export-provided data. However, many existing methods on this topic neglect to check the confidence of the driving actions and the ability to recover from driving mistakes. In this paper, we develop an uncertainty-aware end-to-end trajectory generation method based on imitation learning. It can extract spatiotemporal features from the front-view camera images for scene understanding, and then generate collision-free trajectories several seconds into the future. The experimental results suggest that under various weather and lighting conditions, our network can reliably generate trajectories in different urban environments, such as turning at intersections and slowing down for collision avoidance. Furthermore, closed-loop driving tests suggest that the proposed method achieves better cross-scene/platform driving results than the state-of-the-art (SOTA) end-to-end control method, where our model can recover from off-center and off-orientation errors and capture 80% of dangerous cases with high uncertainty estimations.

2. 

   Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments With Multimodal Sensor Fusion

   Peide Cai, Sukai Wang, Yuxiang Sun, and Ming Liu

   IEEE Robotics and Automation Letters (RA-L) &
   IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS​) , 2020

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   All-day and all-weather navigation is a critical capability for autonomous driving, which requires proper reaction to varied environmental conditions and complex agent behaviors. Recently, with the rise of deep learning, end-to-end control for autonomous vehicles has been studied a lot. However, most works are solely based on visual information, which could be degraded by challenging illumination conditions, such as dim lights or total darkness. In addition, they usually generate and apply deterministic control commands without considering the uncertainties in the future. In this paper, based on imitation learning, we propose a probabilistic driving model with multi-perception capability utilizing the information from camera, lidar and radar. We further evaluate its driving performance on-line on our new driving benchmark, which includes various environments (urban and rural areas, weathers, time of day, etc) and dynamic obstacles (vehicles, pedestrians, motorcyclists, bicyclists, etc). The results suggest that our proposed model outperforms baselines and achieves excellent generalization performance in unseen environments with heavy traffic and extreme weather conditions.

3. 

   High-Speed Autonomous Drifting With Deep Reinforcement Learning

   Peide Cai*, Xiaodong Mei*, Lei Tai, Yuxiang Sun, and Ming Liu

   IEEE Robotics and Automation Letters (RA-L) &
   IEEE International Conference on Robotics and Automation (ICRA) , 2020

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   Drifting is a complicated task for autonomous vehicle control. Most traditional methods in this area are based on motion equations derived by the understanding of vehicle dynamics, which is difficult to be modeled precisely. We propose a robust drift controller without explicit motion equations, which is based on the latest model-free deep reinforcement learning algorithm soft actor-critic. The drift control problem is formulated as a trajectory following task, where the error-based state and reward are designed. After being trained on tracks with different levels of difficulty, our controller is capable of making the vehicle drift through various sharp corners quickly and stably in the unseen map. The proposed controller is further shown to have excellent generalization ability, which can directly handle unseen vehicle types with different physical properties, such as mass, tire friction, etc.

4. 

   SNE-RoadSeg: Incorporating surface normal information into semantic segmentation for accurate freespace detection

   Rui Fan*, Hengli Wang*, Peide Cai, and Ming Liu

   In European Conference on Computer Vision (ECCV) , 2020

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2019

1. 

   Vision-Based Trajectory Planning via Imitation Learning for Autonomous Vehicles

   Peide Cai, Yuxiang Sun, Yuying Chen, and Ming Liu

   In IEEE Intelligent Transportation Systems Conference (ITSC) , 2019

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   Reliable trajectory planning like human drivers in real-world dynamic urban environments is a critical capability for autonomous driving. To this end, we develop a vision and imitation learning-based planner to generate collision-free trajectories several seconds into the future. Our network consists of three sub-networks to conduct three basic driving tasks: keep straight, turn left and turn right. During the planning process, high-level commands are received as prior information to select a specific sub-network. We create our dataset from the Robotcar dataset, and the experimental results suggest that our planner is able to reliably generate trajectories in various driving tasks, such as turning at different intersections, lane-keeping on curved roads and changing lanes for collision avoidance.