Object Heights

README.md

@@ -211,16 +211,41 @@ We are open-sourcing a policy trained on 1,000 randomly sampled scenarios. You c
 
 ### Download the dataset
 
-Two versions of the dataset are available:
+- Two versions of the dataset are available, a [mini version](https://huggingface.co/datasets/EMERGE-lab/GPUDrive_mini) with a 1000 training files and 300 test/validation files, and the [full sized dataset](https://huggingface.co/datasets/EMERGE-lab/GPUDrive) with over a 100k unique scenes. 
+- Replace 'GPUDrive_mini' with 'GPUDrive' below if you wish to download the full dataset.
+- To download the dataset you need the huggingface_hub library:
+```bash
+pip install huggingface_hub
+```
+Then you can download the dataset using python or just `huggingface-cli`.
+
+Option 1: Using Python:
+```python
+>>> from huggingface_hub import snapshot_download
+>>> snapshot_download(repo_id="EMERGE-lab/GPUDrive_mini", repo_type="dataset", local_dir="data/processed")
+```
+Option 2: Use the huggingface-cli:
+
+1. Log in to your Hugging Face account:
+```bash
+huggingface-cli login
+```
+
+2. Download the dataset:
+```bash
+huggingface-cli download EMERGE-lab/GPUDrive_mini --local-dir data/processed --repo-type "dataset"
+```
+
+Option 3: Manual Download:
 
-- a mini-one that is about 1 GB and consists of 1000 training files and 100 validation / test files at: [Dropbox Link](https://www.dropbox.com/sh/8mxue9rdoizen3h/AADGRrHYBb86pZvDnHplDGvXa?dl=0).
-- the full dataset (150 GB) and consists of 134453 training files and 12205 validation / test files: [Dropbox Link](https://www.dropbox.com/sh/wv75pjd8phxizj3/AABfNPWfjQdoTWvdVxsAjUL_a?dl=0)
+1. Visit https://huggingface.co/datasets/EMERGE-lab/GPUDrive_mini
+2. Navigate to the Files and versions tab.
+3. Download the desired files/directories.
 
-The simulator supports initializing scenes from the `Nocturne` dataset. The input parameter for the simulator `json_path` takes in a path to a directory containing the files in the Nocturne format. The `SceneConfig` dataclass in `pygpudrive/env/config.py` dataclass is used to configure how scenes are selected from a folder with traffic scenarios.
 
 ### Re-building the dataset
 
-GPUDrive is compatible with the complete [Waymo Open Motion Dataset](https://github.com/waymo-research/waymo-open-dataset), which contains over 100,000 scenarios. To download new files and create scenarios for the simulator, follow these three steps.
+If you wish to manually generate the dataset, GPUDrive is compatible with the complete [Waymo Open Motion Dataset](https://github.com/waymo-research/waymo-open-dataset), which contains over 100,000 scenarios. To download new files and create scenarios for the simulator, follow these three steps.
 
 1. First, head to [https://waymo.com/open/](https://waymo.com/open/) and click on the "download" button a the top. After registering, click on the files from `v1.2.1 March 2024`, the newest version of the dataset at the time of wrting (10/2024). This will lead you a Google Cloud page. From here, you should see a folder structure like this:
 

pygpudrive/datatypes/observation.py

@@ -14,6 +14,7 @@ class LocalEgoState:
         speed: Speed of the agent in relative coordinates.
         vehicle_length: Length of the agent's bounding box.
         vehicle_width: Width of the agent's bounding box.
+        vehicle_height: Height of the agent's bounding box.
         rel_goal_x: Relative x-coordinate to the goal.
         rel_goal_y: Relative y-coordinate to the goal.
         is_collided: Whether the agent is in collision with another object.
@@ -25,10 +26,11 @@ def __init__(self, self_obs_tensor: torch.Tensor):
         self.speed = self_obs_tensor[:, :, 0]
         self.vehicle_length = self_obs_tensor[:, :, 1]
         self.vehicle_width = self_obs_tensor[:, :, 2]
-        self.rel_goal_x = self_obs_tensor[:, :, 3]
-        self.rel_goal_y = self_obs_tensor[:, :, 4]
-        self.is_collided = self_obs_tensor[:, :, 5]
-        self.id = self_obs_tensor[:, :, 6]
+        self.vehicle_height = self_obs_tensor[:, :, 3]
+        self.rel_goal_x = self_obs_tensor[:, :, 4]
+        self.rel_goal_y = self_obs_tensor[:, :, 5]
+        self.is_collided = self_obs_tensor[:, :, 6]
+        self.id = self_obs_tensor[:, :, 7]
 
     @classmethod
     def from_tensor(
@@ -48,6 +50,7 @@ def normalize(self):
         self.speed = self.speed / constants.MAX_SPEED
         self.vehicle_length = self.vehicle_length / constants.MAX_VEH_LEN
         self.vehicle_width = self.vehicle_width / constants.MAX_VEH_WIDTH
+        self.vehicle_height = self.vehicle_height / constants.MAX_VEH_HEIGHT
         self.rel_goal_x = normalize_min_max(
             tensor=self.rel_goal_x,
             min_val=constants.MIN_REL_GOAL_COORD,
@@ -70,7 +73,7 @@ def shape(self) -> tuple[int, ...]:
 class GlobalEgoState:
     """A class to represent the ego state of the agent in global coordinates.
     Initialized from abs_self_obs_tensor (src/bindings). For details, see
-    `AbsoluteSelfObservation` in src/types.hpp. Shape: (num_worlds, max_agents, 13).
+    `AbsoluteSelfObservation` in src/types.hpp. Shape: (num_worlds, max_agents, 14).
 
     Attributes:
         pos_x: Global x-coordinate of the agent.
@@ -82,6 +85,7 @@ class GlobalEgoState:
         goal_y: Global y-coordinate of the goal.
         vehicle_length: Length of the agent's bounding box.
         vehicle_width: Width of the agent's bounding box.
+        vehicle_height: Height of the agent's bounding box.
         id: Unique identifier of the agent.
     """
 
@@ -96,7 +100,8 @@ def __init__(self, abs_self_obs_tensor: torch.Tensor):
         self.goal_y = abs_self_obs_tensor[:, :, 9]
         self.vehicle_length = abs_self_obs_tensor[:, :, 10]
         self.vehicle_width = abs_self_obs_tensor[:, :, 11]
-        self.id = abs_self_obs_tensor[:, :, 12]
+        self.vehicle_height = abs_self_obs_tensor[:, :, 12]
+        self.id = abs_self_obs_tensor[:, :, 13]
 
     @classmethod
     def from_tensor(
@@ -130,6 +135,7 @@ class PartnerObs:
     orientation: torch.Tensor
     vehicle_length: torch.Tensor
     vehicle_width: torch.Tensor
+    vehicle_height: torch.Tensor
     agent_type: torch.Tensor
     ids: torch.Tensor
 
@@ -148,8 +154,9 @@ def __init__(self, partner_obs_tensor: torch.Tensor):
         self.orientation = partner_obs_tensor[:, :, :, 3].unsqueeze(-1)
         self.vehicle_length = partner_obs_tensor[:, :, :, 4].unsqueeze(-1)
         self.vehicle_width = partner_obs_tensor[:, :, :, 5].unsqueeze(-1)
-        self.agent_type = partner_obs_tensor[:, :, :, 6].unsqueeze(-1)
-        self.ids = partner_obs_tensor[:, :, :, 7].unsqueeze(-1)
+        self.vehicle_height = partner_obs_tensor[:, :, :, 6].unsqueeze(-1)        
+        self.agent_type = partner_obs_tensor[:, :, :, 7].unsqueeze(-1)
+        self.ids = partner_obs_tensor[:, :, :, 8].unsqueeze(-1)
 
     @classmethod
     def from_tensor(
@@ -180,6 +187,7 @@ def normalize(self):
         self.orientation = self.orientation / constants.MAX_ORIENTATION_RAD
         self.vehicle_length = self.vehicle_length / constants.MAX_VEH_LEN
         self.vehicle_width = self.vehicle_width / constants.MAX_VEH_WIDTH
+        self.vehicle_heights = self.vehicle_heights / constants.MAX_VEH_HEIGHT
         self.agent_type = self.agent_type.long()
         self.ids = self.ids
 

pygpudrive/env/constants.py

@@ -6,6 +6,7 @@
 MAX_SPEED = 100
 MAX_VEH_LEN = 30
 MAX_VEH_WIDTH = 10
+MAX_VEH_HEIGHT = 3
 MIN_REL_GOAL_COORD = -1000
 MAX_REL_GOAL_COORD = 1000
 MIN_REL_AGENT_POS = -1000

src/init.hpp

@@ -22,8 +22,7 @@ namespace gpudrive
     struct MapObject
     {
         MapVector2 position[MAX_POSITIONS];
-        float width;
-        float length;
+        VehicleSize vehicle_size;
         float heading[MAX_POSITIONS];
         MapVector2 velocity[MAX_POSITIONS];
         bool valid[MAX_POSITIONS];

src/json_serialization.hpp

@@ -32,8 +32,9 @@ namespace gpudrive
             }
         }
         obj.numPositions = i;
-        j.at("width").get_to(obj.width);
-        j.at("length").get_to(obj.length);
+        j.at("width").get_to(obj.vehicle_size.width);
+        j.at("length").get_to(obj.vehicle_size.length);
+        j.at("height").get_to(obj.vehicle_size.height);
 
         i = 0;
         for (const auto &h : j.at("heading"))

src/level_gen.cpp

@@ -121,8 +121,8 @@ static inline Entity createAgent(Engine &ctx, const MapObject &agentInit) {
     auto agent = ctx.makeRenderableEntity<Agent>();
     auto agent_iface = ctx.get<AgentInterfaceEntity>(agent).e = ctx.makeEntity<AgentInterface>();
 
-    ctx.get<VehicleSize>(agent) = {.length = agentInit.length, .width = agentInit.width};
-    ctx.get<Scale>(agent) = Diag3x3{.d0 = agentInit.length/2, .d1 = agentInit.width/2, .d2 = 1};
+    ctx.get<VehicleSize>(agent) = agentInit.vehicle_size;
+    ctx.get<Scale>(agent) = Diag3x3{.d0 = agentInit.vehicle_size.length/2, .d1 = agentInit.vehicle_size.width/2, .d2 = 1};
     ctx.get<Scale>(agent) *= consts::vehicleLengthScale;
     ctx.get<ObjectID>(agent) = ObjectID{(int32_t)SimObject::Agent};
     ctx.get<EntityType>(agent) = agentInit.type;

src/types.hpp

@@ -64,14 +64,16 @@ namespace gpudrive
         NUM_TYPES = 21,
     };
 
-struct AgentID {
-    int32_t id;
-};
+    struct AgentID 
+    {
+        int32_t id;
+    };
 
     struct VehicleSize
     {
         float length;
         float width;
+        float height;
     };
 
     struct Goal
@@ -179,19 +181,19 @@ struct AgentID {
         VehicleSize vehicle_size;
         Goal goal;
         float collisionState;
-    float id;
+        float id;
         static inline SelfObservation zero()
         {
             return SelfObservation{
                 .speed = 0,
-                .vehicle_size = {0, 0},
+                .vehicle_size = {0, 0, 0},
                 .goal = {.position = {0, 0}},
                 .collisionState = 0,
-            .id = -1};
+                .id = -1};
         }
     };
 
-    const size_t SelfObservationExportSize = 7;
+    const size_t SelfObservationExportSize = 8; // 1 + 3 + 2 + 1 + 1
 
     static_assert(sizeof(SelfObservation) == sizeof(float) * SelfObservationExportSize);
 
@@ -217,7 +219,7 @@ struct AgentID {
         }
     };
 
-    const size_t MapObservationExportSize = 9;
+    const size_t MapObservationExportSize = 9; // 2 + 3 + 1 + 1 + 1 + 1
 
     static_assert(sizeof(MapObservation) == sizeof(float) * MapObservationExportSize);
 
@@ -228,37 +230,37 @@ struct AgentID {
         float heading;
         VehicleSize vehicle_size;
         float type;
-    float id;
-
-    static inline PartnerObservation zero() {
-        return PartnerObservation{
-            .speed = 0,
-            .position = {0, 0},
-            .heading = 0,
-            .vehicle_size = {0, 0},
-            .type = static_cast<float>(EntityType::None),
-            .id = -1};
-    }
-};
+        float id;
 
-    struct RoadMapId{
-        int32_t id;
+        static inline PartnerObservation zero() {
+            return PartnerObservation{
+                .speed = 0,
+                .position = {0, 0},
+                .heading = 0,
+                .vehicle_size = {0, 0, 0},
+                .type = static_cast<float>(EntityType::None),
+                .id = -1};
+        }
     };
 
-    const size_t RoadMapIdExportSize = 1;
-
-    static_assert(sizeof(RoadMapId) == sizeof(int) * RoadMapIdExportSize);
-
     // Egocentric observations of other agents
     struct PartnerObservations
     {
         PartnerObservation obs[consts::kMaxAgentCount - 1];
     };
 
-    const size_t PartnerObservationExportSize = 8;
+    const size_t PartnerObservationExportSize = 9; // 1 + 2 + 1 + 3 + 1 + 1
 
     static_assert(sizeof(PartnerObservations) == sizeof(float) *
-                                                     (consts::kMaxAgentCount - 1) * PartnerObservationExportSize);
+        (consts::kMaxAgentCount - 1) * PartnerObservationExportSize);
+
+    struct RoadMapId{
+        int32_t id;
+    };
+
+    const size_t RoadMapIdExportSize = 1;
+
+    static_assert(sizeof(RoadMapId) == sizeof(int) * RoadMapIdExportSize);
 
     struct AgentMapObservations
     {
@@ -340,7 +342,7 @@ struct AgentID {
 
     struct AbsoluteRotation
     {
-        Rotation rotationAsQuat;
+        Rotation rotationAsQuat; // x, y, z, w
         float rotationFromAxis;
     };
 
@@ -353,7 +355,7 @@ struct AgentID {
         float id;
     };
 
-    const size_t AbsoluteSelfObservationExportSize = 13; // 3 + 4 + 1 + 2 + 2
+    const size_t AbsoluteSelfObservationExportSize = 14; // 3 + 5 + 2 + 3 + 1
 
     static_assert(sizeof(AbsoluteSelfObservation) == sizeof(float) * AbsoluteSelfObservationExportSize);