added coverage test

.travis.yml

@@ -15,5 +15,9 @@ script:
   - python tests/test_supervisor.py #or py.test for Python version 3.5 and below
   - python tests/test_q_lidar.py #or py.test for Python version 3.5 and below
   - python tests/test_traj_class.py #or py.test for Python version 3.5 and below
+  - python tests/test_pedestrians.py #or py.test for Python version 3.5 and below
   - cd docs && make html
 
+after_success:
+  - codecov
+

CONTRIBUTING.md

@@ -0,0 +1,14 @@
+# Contributing
+
+These are the guidelines for contributing code to the FLUIDS repository.
+Please read those before starting an issue or a pull request.
+
+## Issues
+
+Report build issues, usage issues, bugs, and feature requests in GitHub Issues. Please consult [documentation](https://readthedocs.org/projects/urban-driving-simulator/) first. 
+
+When reporting an issue, please try to provide steps to reproduce it. If you have some small test code to demonstrate, you include it in the issue.
+
+## Pull Requests
+
+We welcoming all contributions to FLUIDs. Pull requests consisting of multpile commits should be squashed into one concise commit outlining all changes.

configs/default_config.json

@@ -13,11 +13,12 @@
         "state_space":"Q-LIDAR",
         "use_traffic_lights":true,
         "use_pedestrians":false,
-        "number_of_pedestrians":0,
+        "number_of_pedestrians":4,
         "agent_mappings":{
             "Car":"PlanningPursuitAgent",
             "TrafficLight":"TrafficLightAgent",
-            "CrosswalkLight":"CrosswalkLightAgent"
+            "CrosswalkLight":"CrosswalkLightAgent",
+            "Pedestrian":"PedestrianAgent"
         }
     },
 

docs/source/agents.rst

@@ -34,3 +34,10 @@ Used for a human supervisor to operate the vehicle. Currently, the use of keyboa
 
 .. autoclass:: gym_urbandriving.agents.tele_op.keyboard_agent.KeyboardAgent
    :members: eval_policy
+
+Pedestrian
+^^^^^^^^^^
+FLUIDS also packages a basic pedestrian controller for controling the movement of background pedestrians. Pedestrians display simple behavior, walking forwards unless blocked by a crosswalk signal.
+
+.. autoclass:: gym_urbandriving.agents.supervisor.pedestrian_supervisor.PedestrianAgent
+   :members: eval_policy

docs/source/observations.rst

@@ -4,7 +4,7 @@ Observations are generated for each controlled car in the scene. The type of obs
 
 Raw
 ^^^
-A pointer to the raw environment, giving agents full access to the scene, and all other objects in the scene.
+A copy of the the raw environment, giving agents full access to the scene, and all other objects in the scene. Using this observation type should be avoided, as duplicating the environment incurs a significant performance penalty.
 
 Q-LIDAR
 ^^^^^^^

examples/control_with_supervisor.py

@@ -4,9 +4,8 @@
 from gym_urbandriving import *
 from gym_urbandriving.agents import *
 from gym_urbandriving.assets import *
-from gym_urbandriving.planning import Trajectory
+from gym_urbandriving.utils import Trajectory
 import numpy as np
-import IPython
 
 with open('configs/default_config.json') as json_data_file:
     data = json.load(json_data_file)

examples/json_setup.py

@@ -4,9 +4,8 @@
 from gym_urbandriving import *
 from gym_urbandriving.agents import *
 from gym_urbandriving.assets import *
-from gym_urbandriving.planning import Trajectory
+from gym_urbandriving.utils import Trajectory
 import numpy as np
-import IPython
 
 with open('configs/default_config.json') as json_data_file:
     data = json.load(json_data_file)