In the context of autonomous driving, learning-based methods have been promising for the development of planning modules. During the training process of planning modules, directly minimizing the discrepancy between expert-driving logs and planning output is widely deployed. In general, driving logs consist of suddenly appearing obstacles or swiftly changing traffic signals, which typically necessitate swift and nuanced adjustments in driving maneuvers. Concurrently, future trajectories of the vehicles exhibit their long-term decisions, such as adhering to a reference lane or circumventing stationary obstacles. Due to the unpredictable influence of future events in driving logs, reasoning bias could be naturally introduced to learning based planning modules, which leads to a possible degradation of driving performance. To address this issue, we identify the decisions and their corresponding time horizons, and characterize a so-called decision scope by retaining decisions within derivable horizons only, to mitigate the effect of irrational behaviors caused by unpredictable events. Several viable implementations have been proposed, among which batch normalization along the temporal dimension is particularly effective and achieves superior performance. It consistently outperforms baseline methods in terms of driving scores, as demonstrated through closed-loop evaluations on the nuPlan dataset. Essentially, this approach accommodates an plug-and-play feature to enhance the closed-loop performance of other learning-based planning models.
@inproceedings{xin2024generictrajectoryplanningmethod,title={PlanScope: Learning to Plan Within Decision Scope Does Matter},author={Xin, Ren and Cheng, Jie and Liu, Hongji and Ma, Jun},booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},year={2025},eprint={2411.00476},archiveprefix={arXiv},primaryclass={cs.RO},url={https://arxiv.org/abs/2411.00476},note={in submission}}
2024
A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots
Ren Xin, Hongji Liu, Yingbing Chen, Jie Cheng, Sheng Wang, and 2 more authors
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2024
This paper presents a generic trajectory planning method for constrained all-wheel-steering robots. The proposed method is based on the concept of the velocity field, which is a novel representation of the traveling cost in the robot’s workspace. The velocity field is defined as a vector field that assigns a velocity vector to each point in the workspace, representing the optimal velocity for the robot to reach that point. The velocity field is computed using a novel iterative method that takes into account the robot’s kinematic constraints and the obstacles in the workspace. The trajectory planning problem is then formulated as a path optimization problem, where the goal is to find a path that minimizes the integral of the velocity field along the path. The proposed method is evaluated in simulation and on a real robot, and the results show that it can generate smooth and collision-free trajectories for all-wheel-steering robots in complex environments.
@inproceedings{xin2024generictrajectoryplanningmethoe,title={A Generic Trajectory Planning Method for Constrained All-Wheel-Steering Robots},author={Xin, Ren and Liu, Hongji and Chen, Yingbing and Cheng, Jie and Wang, Sheng and Ma, Jun and Liu, Ming},booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},year={2024},eprint={2404.09677},archiveprefix={arXiv},primaryclass={cs.RO},url={https://arxiv.org/abs/2404.09677},note={}}
RiskMap: A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment
Ren Xin, Sheng Wang, Yingbing Chen, Jie Cheng, Ming Liu, and 1 more author
In Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2024
@inproceedings{xin2024riskmapunifieddrivingcontext,title={{RiskMap:} A Unified Driving Context Representation for Autonomous Motion Planning in Urban Driving Environment},author={Xin, Ren and Wang, Sheng and Chen, Yingbing and Cheng, Jie and Liu, Ming and Ma, Jun},booktitle={Proceedings of the IEEE International Conference on Robotics and Biomimetics},year={2024},eprint={2406.04451},archiveprefix={arXiv},primaryclass={cs.RO},url={https://arxiv.org/abs/2406.04451},}
Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle
Yingbing Chen, Jie Cheng, Sheng Wang, Hongji Liu, Xiaodong Mei, and 12 more authors
@article{10623822,author={Chen, Yingbing and Cheng, Jie and Wang, Sheng and Liu, Hongji and Mei, Xiaodong and Yan, Xiaoyang and Tang, Mingkai and Sun, Ge and Wen, Ya and Cai, Junwei and Xie, Xupeng and Gan, Lu and Chao, Mandan and Xin, Ren and Wang, Lujia and Liu, Ming and Jiao, Jianhao},journal={IEEE Robotics & Automation Magazine},title={Enhancing Campus Mobility: Achievements and Challenges of the Snow Lion Autonomous Shuttle},year={2024},volume={},number={},pages={2-13},keywords={Laser radar;Task analysis;Sensors;Point cloud compression;Location awareness;Three-dimensional displays;Planning},doi={10.1109/MRA.2024.3433168},}
Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions
Sheng Wang, Yingbing Chen, Jie Cheng, Xiaodong Mei, Ren Xin, and 2 more authors
In Proceedings of the IEEE International Conference on Robotics and Automation, 2024
@inproceedings{10610154,author={Wang, Sheng and Chen, Yingbing and Cheng, Jie and Mei, Xiaodong and Xin, Ren and Song, Yongkang and Liu, Ming},booktitle={Proceedings of the IEEE International Conference on Robotics and Automation},title={Improving Autonomous Driving Safety with POP: A Framework for Accurate Partially Observed Trajectory Predictions},year={2024},volume={},number={},pages={14450-14456},keywords={Accuracy;Roads;Self-supervised learning;Predictive models;Trajectory;History;Task analysis},doi={10.1109/ICRA57147.2024.10610154},}
Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments
Jianhao Jiao, Ruoyu Geng, Yuanhang Li, Ren Xin, Bowen Yang, and 5 more authors
IEEE Transactions on Automation Science and Engineering, 2024
@article{10620438,author={Jiao, Jianhao and Geng, Ruoyu and Li, Yuanhang and Xin, Ren and Yang, Bowen and Wu, Jin and Wang, Lujia and Liu, Ming and Fan, Rui and Kanoulas, Dimitrios},journal={IEEE Transactions on Automation Science and Engineering},title={Real-Time Metric-Semantic Mapping for Autonomous Navigation in Outdoor Environments},year={2024},volume={},number={},pages={1-12},keywords={Semantics;Navigation;Robots;Robot sensing systems;Sensors;Real-time systems;Location awareness;Autonomous driving;mapping;navigation},doi={10.1109/TASE.2024.3429280},}
2023
Velocity Field: An Informative Traveling Cost Representation for Trajectory Planning
Ren Xin, Jie Cheng, Sheng Wang, and Ming Liu
In Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2023
@inproceedings{10355004,author={Xin, Ren and Cheng, Jie and Wang, Sheng and Liu, Ming},title={{Velocity Field:} An Informative Traveling Cost Representation for Trajectory Planning},booktitle={Proceedings of the IEEE International Conference on Robotics and Biomimetics},year={2023},volume={},number={},pages={1-6},keywords={Costs;Trajectory planning;Planning;Trajectory;Iterative methods;Reliability;Task analysis},doi={10.1109/ROBIO58561.2023.10355004},}
FCUS: Traffic Rule-Aware Vehicle Trajectory Forecasting Using Continuous Unlikelihood and Signal Temporal Logic Feature
Sheng Wang, Ren Xin, Jie Cheng, Xiaodong Mei, and Ming Liu
In Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2023
@inproceedings{10354968,author={Wang, Sheng and Xin, Ren and Cheng, Jie and Mei, Xiaodong and Liu, Ming},title={{FCUS:} Traffic Rule-Aware Vehicle Trajectory Forecasting Using Continuous Unlikelihood and Signal Temporal Logic Feature},booktitle={Proceedings of the IEEE International Conference on Robotics and Biomimetics},year={2023},volume={},number={},pages={1-6},keywords={Biological system modeling;Neural networks;Predictive models;Trajectory;Safety;Forecasting;Task analysis},doi={10.1109/ROBIO58561.2023.10354968},}
2022
MPNP: Multi-Policy Neural Planner for Urban Driving
Jie Cheng, Ren Xin, Sheng Wang, and Ming Liu
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
@inproceedings{9982111,author={Cheng, Jie and Xin, Ren and Wang, Sheng and Liu, Ming},booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},title={MPNP: Multi-Policy Neural Planner for Urban Driving},year={2022},volume={},number={},pages={10549-10554},keywords={Force;Data models;Behavioral sciences;Trajectory;Planning;Intelligent robots},doi={10.1109/IROS47612.2022.9982111},}
Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model
Yingbing Chen, Ren Xin, Jie Cheng, Qingwen Zhang, Xiaodong Mei, and 2 more authors
@article{9894671,author={Chen, Yingbing and Xin, Ren and Cheng, Jie and Zhang, Qingwen and Mei, Xiaodong and Liu, Ming and Wang, Lujia},journal={IEEE Robotics and Automation Letters},title={Efficient Speed Planning for Autonomous Driving in Dynamic Environment With Interaction Point Model},year={2022},volume={7},number={4},pages={11839-11846},keywords={Planning;Trajectory;Computational modeling;Autonomous vehicles;Analytical models;Robots;Behavioral sciences;Autonomous vehicle navigation;integrated planning and learning;motion and path planning},doi={10.1109/LRA.2022.3207555},}
MMFN: Multi-Modal-Fusion-Net for End-to-End Driving
Qingwen Zhang, Mingkai Tang, Ruoyu Geng, Feiyi Chen, Ren Xin, and 1 more author
In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
@inproceedings{9981775,author={Zhang, Qingwen and Tang, Mingkai and Geng, Ruoyu and Chen, Feiyi and Xin, Ren and Wang, Lujia},booktitle={Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems},title={MMFN: Multi-Modal-Fusion-Net for End-to-End Driving},year={2022},volume={},number={},pages={8638-8643},keywords={Point cloud compression;Laser radar;Three-dimensional displays;Navigation;Robot vision systems;Radar imaging;Feature extraction},doi={10.1109/IROS47612.2022.9981775},}
2021
Potential building energy savings by passive strategies combining daytime radiative coolers and thermochromic smart windows
Kaixin Lin, Luke Chao, Hau Him Lee, Ren Xin, Sai Liu, and 4 more authors
Reducing the energy consumed by space cooling and providing outstanding thermally insulated windows are essential requirements for a smart green building. In this study, a new building design concept for envelope/façade is proposed and demonstrated, providing a solution for thermal management in buildings. This new design for the envelope/façade of buildings comprises two major passive and energy-free technologies, a daytime radiative cooler and a thermochromic smart window. The PDMS-silica-silver daytime passive radiative cooler can provide a cooling effect to the indoor environment without energy input, meanwhile the thermochromic smart window using PNIPAm hydrogel can passively modulate the solar irradiance entering buildings through windows. Hence, the energy needed for indoor heating, cooling and lighting can be reduced significantly. To evaluate the energy-saving performance, model houses are built, one of which is assembled using the proposed building design. The orientation effect on the indoor air temperature of the model houses is investigated. Under intensive solar irradiance, a maximum reduction of 4.8 °C of the indoor air temperature is achieved in the model house constructed using the proposed building design. An energy saving of about 17% of air-conditioning systems in buildings constructed with this new design is expected during daytime operation.
2020
Time reversal damage localization method of ocean platform based on particle swarm optimization algorithm
Weilei Mu, Jiangang Sun, Ren Xin, Guijie Liu, and Shuqing Wang
The traditional time reversal is considered a promising approach for non-destructive testing and health monitoring of key region and structure, but it is considerably time consuming. This paper presents a time reversal damage localization method, based on particle swarm optimization algorithm, which is capable of improving the real-time performance of health monitoring in ocean platform. Firstly, the virtual focusing model of time reversal is constructed, and a succinct expression of virtual focusing for sensor pairs is proposed. Then, on the definition of the evaluation index, the PSO based time reversal algorithm is proposed, and the proper coefficients is given. Finally, the finite element simulation and experimental case validate that the proposed method is capable of find the damage location within limit iterative steps. Thus, the proposed method is a hopeful method for online monitoring and damage localization of large sized structure.
2019
A Design of Hull Coating Robot Based on Mecanum Wheel and Electromagnet
Ren Xin, Zhenxing Zou, and Weilei Mu
In Proceedings of the 2019 International Conference on Intelligent Computing, Automation and Systems, 2019
@inproceedings{9051079,author={Xin, Ren and Zou, Zhenxing and Mu, Weilei},booktitle={Proceedings of the 2019 International Conference on Intelligent Computing, Automation and Systems},title={A Design of Hull Coating Robot Based on Mecanum Wheel and Electromagnet},year={2019},volume={},number={},pages={697-702},keywords={hull coating, Mecanum wheel, electromagnetic force, suspension system},doi={10.1109/ICICAS48597.2019.00151},}
