Vehicle-to-Vehicle (V2V) has received increasing attention with the development of autonomous driving technology. It is believed that multi-vehicular and multi-informative algorithm is the direction of the autonomous driving technology. However, the stability and liability of the communication prevents the future from extensively embracing V2V-based transportation. Rigorous test is required before V2V can actually hit the road. Compared with the costly field test, simulation tests are more economical and feasible. To simulate the V2V communication and evaluate the robustness of current V2V-based algorithm, we are therefore developing a simulation platform integrating different commercial software like SUMO, Veins and OMNET++. These software simulate on the actual New York map, and simulate the vehicular communication in different scenarios and platoon configurations. Our next step is to use this platform to test our own V2V-based algorithms. The output of this research will eventually provide an open platform which would automatically evaluate personally designed algorithm with least manual work.

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Autonomous driving is developing rapidly. A lot of breakthroughs of autonomous driving have emerged in both academy and industry. However, many traffic accidents related to autonomous driving also occur and cause people’s concern on the safety issue of AV. To ensure safety and reliability, rigorous test and simulation is required before AV can really drive on road. For AV test and simulation, realistic data is an essential component. Comprehensive, multi-regime and sufficient self-driving data would definitely help the AV development.

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Columbia Data Science Institute (DSI) Scholars Program

The DSI Scholars Program is to engage and support undergraduate and master students in participating data science related research with Columbia faculty. The program’s unique enrichment activities will foster a learning and collaborative community in data science at Columbia.

Columbia University DSI

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