TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

Zhao, Junqiao; Wang, Hongzhi; Yan, Wu; Guan, Linting; Cai, Le-Wen; Sun, Lu; Yang, Tao; He, Xueming; Zhang, Liangpei; Ding, Yongchao; Zhang, Xinglian; Wang, Xinchen; Huang, Jinlin; Zhang, Enwei; Huang, Yewei; Jiang, Wei; Zhang, Shaoming; Liu, Xiong; Feng, Tiantian

doi:10.1109/itsc.2018.8569629

Cited by 9 publications

(5 citation statements)

References 20 publications

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“…2) TJ-TiEV Dataset: To evaluate the proposed pole-curb fusion vehicle localization system, we collected our own TJ-TiEV dataset [36]. The platform for data collection is TiEV from Tongji University and the data collection scenario is located in the Jiading campus of Tongji University.…”

Section: Methodsmentioning

confidence: 99%

Pole-Curb Fusion Based Robust and Efficient Autonomous Vehicle Localization System With Branch-and-Bound Global Optimization and Local Grid Map Method

Chen

Lü

et al. 2021

IEEE Trans. Veh. Technol.

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The localization system is one of the key components of autonomous vehicles. Widely-used GNSS (low-cost) alone can not meet the centimeter accuracy and is unstable in urban environment. Typically LiDAR-based localization system is very computationally demanding due to the usage of the dense point cloud map. In this paper, we propose a novel lightweight LiDAR-based localization system for autonomous vehicle in this paper. The proposed system only relies on lightweight poles and curbs landmark map, which is highly robust and efficient compared to other localization systems. Poles and curbs are selected as landmarks because of their commonality and stability. We novelly propose a Branchand-Bound (BnB)-based global optimization method to tackle the data association problem of poles. Motion decoupling is adopted to decouple translation and rotation to improve the efficiency of the BnB-based algorithm. Besides, we propose a new local grid map-based representation for curbs to make better use of curb information. Cost functions for pole and curb are defined respectively and then fused for the subsequent non-linear optimization method to obtain the vehicle location. Experiments on KITTI dataset and our self-collected dataset demonstrate the efficiency and accuracy of our system.

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Section: Methodsmentioning

confidence: 99%

Pole-Curb Fusion Based Robust and Efficient Autonomous Vehicle Localization System With Branch-and-Bound Global Optimization and Local Grid Map Method

Chen

Lü

et al. 2021

IEEE Trans. Veh. Technol.

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“…Our data collecting platform is a refitted electric vehicle in Fig 2 that is capable of fully autonomous driving named Tongji intelligent EV, i.e., TiEV [47]. TiEV is equipped with several types of sensors.…”

Section: A System Setupmentioning

confidence: 99%

Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods

Chen

Wang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Detecting and locating surrounding vehicles robustly and efficiently are essential capabilities for autonomous vehicles. Existing solutions often rely on vision-based methods or 3D LiDAR-based methods. These methods are either too expensive in both sensor pricing (3D LiDAR) and computation (camera and 3D LiDAR) or less robust in resisting harsh environment changes (camera). In this work, we revisit the LiDAR based approaches for vehicle detection with a less expensive 2D LiDAR by utilizing modern deep learning approaches. We aim at filling in the gap as few previous works conclude an efficient and robust vehicle detection solution in a deep learning way in 2D. To this end, we propose a learning based method with the input of pseudo-images, named Cascade Pyramid Region Proposal Convolution Neural Network (Cascade Pyramid RCNN), and a hybrid learning method with the input of sparse points, named Hybrid Resnet Lite. Experiments are conducted with our newly 2D LiDAR vehicle dataset recorded in complex traffic environments. Results demonstrate that the Cascade Pyramid RCNN outperforms state-of-the-art methods in accuracy while the proposed Hybrid Resnet Lite provides superior performance of the speed and lightweight model by hybridizing learning based and non-learning based modules. As few previous works conclude an efficient and robust vehicle detection solution with 2D LiDAR, our research fills in this gap and illustrates that even with limited sensing source from a 2D LiDAR, detecting obstacles like vehicles efficiently and robustly is still achievable.

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“…In the last decade, various challenges, such as the wellknown DARPA Urban Challenge and the Intelligent Vehicle Future Challenge (IVFC) have proven that autonomous driving can be a reality in the near future. The teams participating in these events have demonstrated numerous technical frameworks for autonomous driving [36,43,44,51]. Nowadays, most self-driving vehicles are geared up with multiple high-precision sensors such as LIDAR and cameras.…”

Section: Introductionmentioning

confidence: 99%

Deep reinforcement learning based control for Autonomous Vehicles in CARLA

Pérez-Gil

Barea

López-Guillén

et al. 2022

Multimed Tools Appl

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Nowadays, Artificial Intelligence (AI) is growing by leaps and bounds in almost all fields of technology, and Autonomous Vehicles (AV) research is one more of them. This paper proposes the using of algorithms based on Deep Learning (DL) in the control layer of an autonomous vehicle. More specifically, Deep Reinforcement Learning (DRL) algorithms such as Deep Q-Network (DQN) and Deep Deterministic Policy Gradient (DDPG) are implemented in order to compare results between them. The aim of this work is to obtain a trained model, applying a DRL algorithm, able of sending control commands to the vehicle to navigate properly and efficiently following a determined route. In addition, for each of the algorithms, several agents are presented as a solution, so that each of these agents uses different data sources to achieve the vehicle control commands. For this purpose, an open-source simulator such as CARLA is used, providing to the system with the ability to perform a multitude of tests without any risk into an hyper-realistic urban simulation environment, something that is unthinkable in the real world. The results obtained show that both DQN and DDPG reach the goal, but DDPG obtains a better performance. DDPG perfoms trajectories very similar to classic controller as LQR. In both cases RMSE is lower than 0.1m following trajectories with a range 180-700m. To conclude, some conclusions and future works are commented.

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TiEV: The Tongji Intelligent Electric Vehicle in the Intelligent Vehicle Future Challenge of China

Cited by 9 publications

References 20 publications

Pole-Curb Fusion Based Robust and Efficient Autonomous Vehicle Localization System With Branch-and-Bound Global Optimization and Local Grid Map Method

Pole-Curb Fusion Based Robust and Efficient Autonomous Vehicle Localization System With Branch-and-Bound Global Optimization and Local Grid Map Method

Pseudo-Image and Sparse Points: Vehicle Detection With 2D LiDAR Revisited by Deep Learning-Based Methods

Deep reinforcement learning based control for Autonomous Vehicles in CARLA

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