Sajjad Mozaffari

875 total citations · 1 hit paper
15 papers, 607 citations indexed

About

Sajjad Mozaffari is a scholar working on Automotive Engineering, Building and Construction and Control and Systems Engineering. According to data from OpenAlex, Sajjad Mozaffari has authored 15 papers receiving a total of 607 indexed citations (citations by other indexed papers that have themselves been cited), including 8 papers in Automotive Engineering, 4 papers in Building and Construction and 3 papers in Control and Systems Engineering. Recurrent topics in Sajjad Mozaffari's work include Autonomous Vehicle Technology and Safety (8 papers), Traffic Prediction and Management Techniques (4 papers) and Traffic control and management (3 papers). Sajjad Mozaffari is often cited by papers focused on Autonomous Vehicle Technology and Safety (8 papers), Traffic Prediction and Management Techniques (4 papers) and Traffic control and management (3 papers). Sajjad Mozaffari collaborates with scholars based in United Kingdom, Iran and Italy. Sajjad Mozaffari's co-authors include Mehrdad Dianati, Alexandros Mouzakitis, Paul Jennings, Omar Y. Al-Jarrah, Eduardo Arnold, Saber Fallah, Konstantinos Koufos, Roger Woodman, Francesco Bellotti and Hossein Mostafavi and has published in prestigious journals such as Construction and Building Materials, Sensors and IEEE Transactions on Intelligent Transportation Systems.

In The Last Decade

Sajjad Mozaffari

15 papers receiving 585 citations

Hit Papers

1 papers align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review

2020 430 citations Sajjad Mozaffari, Omar Y. Al-Jarrah et al. IEEE Transactions on Intelligent Transportation Systems profile →

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
Sajjad Mozaffari United Kingdom	7	375	164	162	127	121	15	607
Véronique Cherfaoui France	9	303 0.8×	151 0.9×	123 0.8×	104 0.8×	105 0.9×	19	472
Guotao Xie China	12	404 1.1×	162 1.0×	152 0.9×	224 1.8×	80 0.7×	24	612
Felix Klanner Germany	11	312 0.8×	128 0.8×	121 0.7×	151 1.2×	113 0.9×	20	610
Manolis Tsogas Greece	12	360 1.0×	117 0.7×	105 0.6×	138 1.1×	110 0.9×	19	508
ByeoungDo Kim South Korea	6	343 0.9×	142 0.9×	149 0.9×	118 0.9×	260 2.1×	7	604
Rubén Izquierdo Spain	16	308 0.8×	198 1.2×	99 0.6×	82 0.6×	254 2.1×	59	689
Mathias Perrollaz France	8	502 1.3×	222 1.4×	176 1.1×	184 1.4×	133 1.1×	13	651
Sebastian Brechtel Germany	8	527 1.4×	203 1.2×	133 0.8×	244 1.9×	171 1.4×	9	681
Jürgen Wiest Germany	9	274 0.7×	168 1.0×	89 0.5×	79 0.6×	139 1.1×	12	456
Florian Kuhnt Germany	11	373 1.0×	171 1.0×	65 0.4×	161 1.3×	78 0.6×	17	519

Countries citing papers authored by Sajjad Mozaffari

Since Specialization

Citations

This map shows the geographic impact of Sajjad Mozaffari's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Sajjad Mozaffari with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sajjad Mozaffari more than expected).

Fields of papers citing papers by Sajjad Mozaffari

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Sajjad Mozaffari. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Sajjad Mozaffari. The network helps show where Sajjad Mozaffari may publish in the future.

Co-authorship network of co-authors of Sajjad Mozaffari

This figure shows the co-authorship network connecting the top 25 collaborators of Sajjad Mozaffari. A scholar is included among the top collaborators of Sajjad Mozaffari based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Sajjad Mozaffari. Sajjad Mozaffari is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Dianati, Mehrdad, et al.. (2024). Optical Flow Based Detection and Tracking of Moving Objects for Autonomous Vehicles. IEEE Transactions on Intelligent Transportation Systems. 25(9). 12578–12590. 4 indexed citations

Mozaffari, Sajjad, et al.. (2023). Multimodal Manoeuvre and Trajectory Prediction for Automated Driving on Highways Using Transformer Networks. IEEE Robotics and Automation Letters. 8(10). 6123–6130. 25 indexed citations

Bellotti, Francesco, et al.. (2023). Exploiting Big Data for Experiment Reporting: The Hi-Drive Collaborative Research Project Case. Sensors. 23(18). 7866–7866. 4 indexed citations

Mozaffari, Sajjad, et al.. (2023). A Novel Deep Neural Network for Trajectory Prediction in Automated Vehicles Using Velocity Vector Field. 4003–4010. 7 indexed citations

Mozaffari, Sajjad, et al.. (2023). Trajectory Prediction with Observations of Variable-Length for Motion Planning in Highway Merging Scenarios. Warwick Research Archive Portal (University of Warwick). 5633–5640. 8 indexed citations

Mozaffari, Sajjad, et al.. (2022). Oil-well lightweight cement slurry for improving compressive strength and hydration rate in low-temperature conditions. Construction and Building Materials. 357. 129301–129301. 6 indexed citations

Yıldırım, Mustafa, et al.. (2022). Prediction Based Decision Making for Autonomous Highway Driving. 2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC). 138–145. 4 indexed citations

Mozaffari, Sajjad, Eduardo Arnold, Mehrdad Dianati, & Saber Fallah. (2022). Early Lane Change Prediction for Automated Driving Systems Using Multi-Task Attention-Based Convolutional Neural Networks. IEEE Transactions on Intelligent Vehicles. 7(3). 758–770. 52 indexed citations

Arnold, Eduardo, Sajjad Mozaffari, & Mehrdad Dianati. (2021). Fast and Robust Registration of Partially Overlapping Point Clouds. IEEE Robotics and Automation Letters. 7(2). 1502–1509. 52 indexed citations

10.

Mozaffari, Sajjad, Eduardo Arnold, Mehrdad Dianati, & Saber Fallah. (2021). A Comparative Study of Ego-centric and Cooperative Perception for Lane Change Prediction in Highway Driving Scenarios. Warwick Research Archive Portal (University of Warwick). 113–121. 1 indexed citations

11.

Mozaffari, Sajjad, Omar Y. Al-Jarrah, Mehrdad Dianati, Paul Jennings, & Alexandros Mouzakitis. (2020). Deep Learning-Based Vehicle Behavior Prediction for Autonomous Driving Applications: A Review. IEEE Transactions on Intelligent Transportation Systems. 23(1). 33–47. 430 indexed citations breakdown →

12.

Bellotti, Francesco, Eduardo Arnold, Sajjad Mozaffari, et al.. (2020). Managing Big Data for Addressing Research Questions in a Collaborative Project on Automated Driving Impact Assessment. Sensors. 20(23). 6773–6773. 5 indexed citations

13.

Mozaffari, Sajjad, et al.. (2016). Design & modeling of a novel multi-functional elastic actuator (MFEA). 16. 32–36. 4 indexed citations

14.

Mozaffari, Sajjad, et al.. (2015). Highly efficient alpha-beta pruning minimax based Loop Trax Solver on FPGA. 9. 1–4. 3 indexed citations

15.

Mozaffari, Sajjad, et al.. (2014). Highly scalable, shared-memory, Monte-Carlo tree search based Blokus Duo Solver on FPGA. 370–373. 2 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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