Research　in　the　areas　related　to　driving　behavior,　e.g.,　behavior　modeling　and　prediction,　requires　datasets　with　highly　interactive　vehicle　motions.　Existing　public　vehicle　motion　datasets　emphasize　increasing　the　number　of　vehicles　and　time　duration,　but　behavior-related　researchers　are　suffering　from　two　factors.　First,　strong　interactions　among　vehicles　are　not　well　addressed　and　datasets　are　of　relatively　low-density　to　observe　meaningful　interactions.　Second,　most　of　the　existing　datasets　are　missing　the　map　information　with　reference　paths　which　is　essential　for　driving-behavior-related　research.　To　address　this　issue,　a　dataset　with　highly　interactive　vehicle　motions　is　constructed　in　this　paper.　A　variety　of　challenging　driving　scenarios　such　as　unsignalized　intersections　and　roundabouts　are　included.　Reference　paths　are　also　constructed　from　motion　data　along　with　high-definition　maps　so　that　key　features　can　be　generated　for　both　prediction　and　planning　algorithms.　Moreover,　we　propose　a　set　of　metrics　to　extract　the　interactive　motions　in　different　maps,　including　the　minimum　difference　of　time　to　collision　point　(MDTTC)　and　duration　of　waiting　period.　Such　metrics　are　used　to　quantity　the　interaction　density　of　the　dataset.　We　also　give　several　representative　results　on　prediction　and　motion　generation　utilizing　the　constructed　dataset　to　demonstrate　how　the　dataset　can　facilitate　research　in　the　area　of　driving　behavior.　©　2019　IEEE.