diff --git a/dataset/src/main/scala/frameless/TypedDataset.scala b/dataset/src/main/scala/frameless/TypedDataset.scala
index 845e52c9..7f459684 100644
--- a/dataset/src/main/scala/frameless/TypedDataset.scala
+++ b/dataset/src/main/scala/frameless/TypedDataset.scala
@@ -1234,20 +1234,30 @@ class TypedDataset[T] protected[frameless](val dataset: Dataset[T])(implicit val
     import org.apache.spark.sql.functions.{explode => sparkExplode, struct => sparkStruct, col => sparkCol}
     val df = dataset.toDF()
 
-    // preserve the original list of columns
-    val columns = df.columns.toSeq.map(sparkCol)
     // select all columns, all original columns and [key, value] columns appeared after the map explode
     // .withColumn(column.value.name, sparkExplode(df(column.value.name))) in this case would not work
     // since the map explode produces two columns
-    val exploded = df.select(sparkCol("*"), sparkExplode(df(column.value.name)))
+    val columnNames = df.columns.toSeq
+    val columnNamesRenamed = columnNames.map(c => s"frameless_$c")
+
+    // preserve the original list of renamed columns
+    val columns = columnNamesRenamed.map(sparkCol)
+
+    val columnRenamed = s"frameless_${column.value.name}"
+    // explode of a map adds "key" and "value" columns into the Row
+    // this may cause col namings collision: row could already contain key / value columns
+    // we rename the original Row columns to avoid this collision
+    val dfr = df.toDF(columnNamesRenamed: _*)
+    val exploded = dfr.select(sparkCol("*"), sparkExplode(dfr(columnRenamed)))
     val trans =
       exploded
         // map explode explodes it into [key, value] columns
         // the only way to put it into a column is to create a struct
-        // TODO: handle org.apache.spark.sql.AnalysisException: Reference 'key / value' is ambiguous, could be: key / value, key / value
-        .withColumn(column.value.name, sparkStruct(exploded("key"), exploded("value")))
+        .withColumn(columnRenamed, sparkStruct(exploded("key"), exploded("value")))
         // selecting only original columns, we don't need [key, value] columns left in the DataFrame after the map explode
         .select(columns: _*)
+        // rename columns back and form the result
+        .toDF(columnNames: _*)
         .as[Out](TypedExpressionEncoder[Out])
     TypedDataset.create[Out](trans)
   }
diff --git a/dataset/src/test/scala/frameless/ExplodeTests.scala b/dataset/src/test/scala/frameless/ExplodeTests.scala
index 205b4903..3078ceb1 100644
--- a/dataset/src/test/scala/frameless/ExplodeTests.scala
+++ b/dataset/src/test/scala/frameless/ExplodeTests.scala
@@ -77,4 +77,19 @@ class ExplodeTests extends TypedDatasetSuite {
     check(forAll(prop[String, Int, Long] _))
     check(forAll(prop[Long, String, Int] _))
   }
+
+  test("explode on maps making sure no key / value naming collision happens") {
+    def prop[K: TypedEncoder: ClassTag, V: TypedEncoder: ClassTag, A: TypedEncoder: ClassTag, B: TypedEncoder: ClassTag](xs: List[X3KV[K, V, Map[A, B]]]): Prop = {
+      val tds = TypedDataset.create(xs)
+
+      val framelessResults = tds.explodeMap('c).collect().run().toVector
+      val scalaResults = xs.flatMap { x3 => x3.c.toList.map((x3.key, x3.value, _)) }.toVector
+
+      framelessResults ?= scalaResults
+    }
+
+    check(forAll(prop[String, Int, Long, String] _))
+    check(forAll(prop[Long, String, Int, Long] _))
+    check(forAll(prop[Int, Long, String, Int] _))
+  }
 }
diff --git a/dataset/src/test/scala/frameless/XN.scala b/dataset/src/test/scala/frameless/XN.scala
index 0aa7f728..c23d4b45 100644
--- a/dataset/src/test/scala/frameless/XN.scala
+++ b/dataset/src/test/scala/frameless/XN.scala
@@ -52,6 +52,19 @@ object X3U {
     Ordering.Tuple3[A, B, C].on(x => (x.a, x.b, x.c))
 }
 
+case class X3KV[A, B, C](key: A, value: B, c: C)
+
+object X3KV {
+  implicit def arbitrary[A: Arbitrary, B: Arbitrary, C: Arbitrary]: Arbitrary[X3KV[A, B, C]] =
+    Arbitrary(Arbitrary.arbTuple3[A, B, C].arbitrary.map((X3KV.apply[A, B, C] _).tupled))
+
+  implicit def cogen[A, B, C](implicit A: Cogen[A], B: Cogen[B], C: Cogen[C]): Cogen[X3KV[A, B, C]] =
+    Cogen.tuple3(A, B, C).contramap(x => (x.key, x.value, x.c))
+
+  implicit def ordering[A: Ordering, B: Ordering, C: Ordering]: Ordering[X3KV[A, B, C]] =
+    Ordering.Tuple3[A, B, C].on(x => (x.key, x.value, x.c))
+}
+
 case class X4[A, B, C, D](a: A, b: B, c: C, d: D)
 
 object X4 {