| 1. |
- Bao, Dongxuan, et al.
(författare)
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A Wirelessly Powered UWB RFID Sensor Tag With Time-Domain Analog-to-Information Interface
- 2018
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Ingår i: IEEE Journal of Solid-State Circuits. - : IEEE. - 0018-9200 .- 1558-173X. ; 53:8, s. 2227-2239
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Tidskriftsartikel (refereegranskat)abstract
- This paper presents a wirelessly powered radio frequency identification sensor tag with an analog-to-information interface. A time-domain interface, incorporating an ultra-lowpower impulse radio ultra-wideband (IR-UWB) transmitter (TX), is employed. The analog signal from the sensor is compared with a triangular waveform, resulting in a pulse-position modulation signal to trigger UWB pulses. Thanks to the high time-resolution IR-UWB radio, time intervals of the impulses can be used to represent the original input value, which is measured remotely on the reader side by a time-of-arrival estimator. This approach not only eliminates the analog-to-digital converter (ADC) but also significantly reduces the number of bits to be transmitted for power saving. The proposed tag is fabricated in a 0.18-mu m CMOS process with an active area of 2.5 mm(2). The measurement results demonstrate that a 300-kS/s sampling rate with a 6.7-bit effective number of bits (ENOB) is obtained via a UWB receiver with a sensitivity of -93 dBm and an integration window of 10 ns. The ENOB is improved to 7.3 bits when the integration window is reduced to 2 ns. The tag can be powered up by a -18-dBm UHF input signal. The power consumption of the proposed tag is 41.5 mu W yielding a 1.3-pJ/conv.step figure of merit, offering 9x and 67x improvements compared with the state of the art based on an ADC and a backscattering TX, and the tag based on an ADC and a narrowband TX, respectively.
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| 2. |
- Lu, Yichen, et al.
(författare)
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Analytical Method of Traffic Conflict at Urban Road Intersections Based on Risk Region
- 2021
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Ingår i: Tongji Daxue Xuebao/Journal of Tongji University. - 0253-374X. ; 49:7, s. 941-948
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Tidskriftsartikel (refereegranskat)abstract
- A two-stage method of traffic conflict analysis was proposed based on risk region. By using the inD dataset, traffic conflicts were recognized with the calculation of time to risk region(tTTR), and then the degree of traffic conflict risk was judged by risk region duration. Compared with the method based on time-to-collision(tTTC), the proposed method can not only identify rear-end conflicts and crossing conflicts more effectively, but also characterize the degree of traffic conflict risk at different times.
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| 3. |
- Yang, Xiaoxue, et al.
(författare)
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Improved deep reinforcement learning for car-following decision-making
- 2023
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Ingår i: Physica A. - : Elsevier B.V.. - 0378-4371 .- 1873-2119. ; 624
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Tidskriftsartikel (refereegranskat)abstract
- Accuracy improvement of Car-following (CF) model has attracted much attention in recent years. Although a few studies incorporate deep reinforcement learning (DRL) to describe CF behaviors, proper design of reward function is still an intractable problem. This study improves the deep deterministic policy gradient (DDPG) car-following model with stacked denoising autoencoders (SDAE), and proposes a data-driven reward representation function, which quantifies the implicit interaction between ego vehicle and preceding vehicle in car-following process. The experimental results demonstrate that DDPG-SDAE model has superior ability of imitating driving behavior: (1) validating effectiveness of the reward representation method with low deviation of trajectory; (2) demonstrating generalization ability on two different trajectory datasets (HighD and SPMD); (3) adapting to three traffic scenarios clustered by a dynamic time warping distance based k-medoids method. Compared with Recurrent Neural Networks (RNN) and intelligent driver model (IDM), DDPG-SDAE model shows better performance on the deviation of speed and relative distance. This study demonstrates superiority of a novel reward extraction method fusing SDAE into DDPG algorithm and provides inspiration for developing driving decision-making model. © 2023 Elsevier B.V.
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| 4. |
- Zhang, Yue, et al.
(författare)
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Identifying dynamic interaction patterns in mandatory and discretionary lane changes using graph structure
- 2023
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Ingår i: Computer-Aided Civil and Infrastructure Engineering. - : John Wiley and Sons Inc. - 1093-9687 .- 1467-8667. ; 39:5, s. 638-
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Tidskriftsartikel (refereegranskat)abstract
- A quantitative understanding of dynamic lane-changing interaction patterns is indispensable for improving the decision-making of autonomous vehicles (AVs), especially in mixed traffic with human-driven vehicles. This paper develops a novel framework combining the hidden Markov model (HMM) and graph structure to identify the difference in dynamic interaction patterns between mandatory lane changes (MLC) and discretionary lane changes (DLC). An HMM is developed to separate the interaction patterns considering heterogeneity in lane-changing processes and reveal the temporal properties of these patterns. Conditional mutual information is used to quantify the interaction intensity, and the graph structure is used to characterize the relationship between vehicles. Finally, a case study is conducted to demonstrate the practical value of the proposed framework and validate its effectiveness in predicting lane-changing trajectories. Based on the lane-changing events extracted from a real-world trajectory dataset, the proposed analytical framework is applied to model MLC and DLC under congested traffic with levels of service E and F. The results show that there could be multiple heterogeneous dynamic interaction patterns in a lane-changing process. A comparison of MLC and DLC demonstrates that MLC involves more intense interactions and more frequent transitions of the interaction network structure, while the evolution rules of interaction patterns in DLC do not exhibit a clear trend. The findings in this study are useful for understanding the connectivity structure between vehicles in lane-changing interactions and for designing safe and smooth driving decision-making models for AVs and advanced driver-assistance systems.
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| 5. |
- Zhang, Yue, et al.
(författare)
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Spatiotemporal Interaction Pattern Recognition and Risk Evolution Analysis During Lane Changes
- 2023
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Ingår i: IEEE Transactions on Intelligent Transportation Systems. - 1524-9050 .- 1558-0016. ; 24:6, s. 6663-6673
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Tidskriftsartikel (refereegranskat)abstract
- In complex lane change (LC) scenarios, semantic interpretation and safety analysis of dynamic interaction pattern are necessary for autonomous vehicles to make appropriate decisions. This study proposes a learning framework that combines primitive-based interaction pattern recognition and risk analysis. The Hidden Markov Model with the Gaussian mixture model (GMM-HMM) approach is developed to decompose the LC scenarios into primitives. Then K-means clustering with Dynamic Time Warping (DTW) is applied to gather the primitives into 13 LC interaction patterns. Finally, this study considers time-to-collision (TTC) of two conflict types involved in the LC process. And the TTC is used to analyze the risk of interaction patterns and extract high-risk LC interaction patterns. The LC events obtained from the Highway Drone Dataset (highD) demonstrate that the identified LC interaction patterns contain interpretable semantic information. This study identifies the dynamic spatiotemporal characteristics and risk formation mechanism of the LC interaction patterns. The findings are useful to comprehensively understand the latent interaction patterns, which can then be used to design and improve the decision-making process during lane changes and enhance the safety of autonomous vehicle.
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