helper functions for exps, and two veh sets for merge

scripts/experiments.jl

@@ -6,93 +6,40 @@ using AutomotiveSimulator
 using AutomotiveInteraction
 using PGFPlots
 
-"""
-function extract_metrics(f;ts=1,id_list=[],dur=10.,modelmaker=nothing,filename=[])
-
-- Perform driving simulation and extract metrics
-
-# Arguments
-- ts: start frame
-- id_list: list of vehicles 
-- dur: duration
-- modelmaker: function that makes the models
-- filename: provide optionally to make comparison video
-
-# Example
-```julia
-f = FilteringEnvironment()
-scenes,collisions = extract_metrics(f,id_list=[4,6,8,13,19,28,29],
-modelmaker=make_cidm_models,filename="media/cidm_exp.mp4")
-```
-"""
-function extract_metrics(f;ts=1,id_list=[],dur=10.,modelmaker=nothing,filename=[])
-    print("Run experiment from scripts being called \n")
-    scene_real = deepcopy(f.traj[ts])
-    if !isempty(id_list) keep_vehicle_subset!(scene_real,id_list) end
-
-    # If modelmaker argument is provided, use that function to create models
-    # Otherwise, obtaine driver models using particle filtering
-    models = Dict{Int64,DriverModel}()
-    if isnothing(modelmaker)
-        print("Let's run particle filtering to create driver models")
-        models, = obtain_driver_models(f,id_list,30,1,30)
-    else
-        print("Lets use idm or c_idm to create driver models\n")
-        models = modelmaker(scene_real)
-    end
-
-    nticks = Int(ceil(dur/f.timestep))
-    scene_list = simulate(scene_real,f.roadway,models,nticks,f.timestep)
-
-    c_array = test_collision(scene_list,id_list)
-
-    truth_list = f.traj[ts:ts+nticks]
-
-    # Make a comparison video if filename provided
-    if !isempty(filename)
-        video_overlay_scenelists(scene_list,truth_list,id_list=id_list,roadway=f.roadway,
-            filename=filename)
-    end
-
-    # rmse dict with vehicle wise rmse values
-    rmse_pos_dict,rmse_vel_dict = compute_rmse(truth_list,scene_list,id_list=id_list)
-
-    # Average over the vehicles
-    rmse_pos = rmse_dict2mean(rmse_pos_dict)
-    rmse_vel = rmse_dict2mean(rmse_vel_dict)
-
-    return rmse_pos,rmse_vel,c_array
-end
+include("helpers.jl")
 
 f = FilteringEnvironment()
-veh_id_list = [4,6,8,13,19,28,29]
+#veh_id_list = [4,6,8,13,19,28,29];ts=1
+veh_id_list = [34,37,40,42,43,49,50];ts=88
 
 print("Get the metrics for particle filering\n")
-pos_pf,vel_pf,collisions_pf = extract_metrics(f,id_list=veh_id_list)
+pos_pf,vel_pf,collisions_pf = extract_metrics(f,ts=ts,id_list=veh_id_list,
+filename="media/pf_ts_88.mp4")
 
 print("Get the metrics for c-idm\n")
-pos_cidm,vel_cidm,collisions_cidm = extract_metrics(f,id_list=veh_id_list,
-modelmaker=make_cidm_models)
+pos_cidm,vel_cidm,collisions_cidm = extract_metrics(f,ts=ts,id_list=veh_id_list,
+modelmaker=make_cidm_models,filename="media/cidm_ts_88.mp4")
 
 print("Get the metrics for idm\n")
-pos_idm,vel_idm,collisions_idm = extract_metrics(f,id_list=veh_id_list,
-modelmaker=make_IDM_models)
+pos_idm,vel_idm,collisions_idm = extract_metrics(f,ts=ts,id_list=veh_id_list,
+modelmaker=make_IDM_models,filename="media/idm_ts_88.mp4")
 
-make_plots=false
+make_plots=true
 if(make_plots)
     # Make rmse_pos plot
-    p_pos_pf = PGFPlots.Plots.Linear(collect(1:length(pos_pf)),pos_cidm,legendentry="pf");
-    p_pos_cidm = PGFPlots.Plots.Linear(collect(1:length(pos_cidm)),pos_cidm,legendentry="cidm");
-    p_pos_idm = PGFPlots.Plots.Linear(collect(1:length(pos_idm)),pos_idm,legendentry="idm");
+    p_pos_pf = pgfplot_vector(pos_pf,leg="pf");
+    p_pos_cidm = pgfplot_vector(pos_cidm,leg="cidm");
+    p_pos_idm = pgfplot_vector(pos_idm,leg="idm");
     ax_rmse_pos = PGFPlots.Axis([p_pos_pf,p_pos_cidm,p_pos_idm],
             xlabel="timestep",ylabel="rmse pos",title="rmse pos: c-IDM vs IDM");
-    PGFPlots.save("media/rmse_pos_cidmvsidm.pdf",ax_rmse_pos);
+    PGFPlots.save("media/rmse_pos.pdf",ax_rmse_pos);
 
     # Make collisions plot
-    coll_pf = PGFPlots.Plots.Linear(collect(1:length(collisions_pf)),collisions_pf,legendentry="pf")
-    coll_idm = PGFPlots.Plots.Linear(collect(1:length(collisions_idm)),collisions_idm,legendentry="idm");
-    coll_cidm = PGFPlots.Plots.Linear(collect(1:length(collisions_cidm)),collisions_cidm,legendentry="cidm");
+    coll_pf = pgfplot_vector(collisions_pf,leg="pf");
+    coll_idm = pgfplot_vector(collisions_cidm,leg="cidm");
+    coll_cidm = pgfplot_vector(collisions_idm,leg="idm");
     ax_coll = PGFPlots.Axis([coll_pf,coll_cidm,coll_idm],
-            xlabel="timestep",ylabel="iscolliding",title="Collision assessment: c-IDM vs IDM");
-    PGFPlots.save("media/coll_cidmvsidm.pdf",ax_coll)
+            xlabel="timestep",ylabel="iscolliding",
+            title="Collision assessment: c-IDM vs IDM");
+    PGFPlots.save("media/coll.pdf",ax_coll)
 end

scripts/helpers.jl

@@ -0,0 +1,73 @@
+# This file provides helper functions to run experiments
+
+"""
+function extract_metrics(f;ts=1,id_list=[],dur=10.,modelmaker=nothing,filename=[])
+
+- Perform driving simulation starting from `ts` for `dur` duration.
+
+# Arguments
+- ts: start frame
+- id_list: list of vehicles 
+- dur: duration
+- modelmaker: function that makes the models
+- filename: provide optionally to make comparison video
+
+# Example
+```julia
+f = FilteringEnvironment()
+scenes,collisions = extract_metrics(f,id_list=[4,6,8,13,19,28,29],
+modelmaker=make_cidm_models,filename="media/cidm_exp.mp4")
+```
+"""
+function extract_metrics(f;ts=1,id_list=[],dur=10.,modelmaker=nothing,filename=[])
+    print("Run experiment from scripts being called \n")
+    scene_real = deepcopy(f.traj[ts])
+    if !isempty(id_list) keep_vehicle_subset!(scene_real,id_list) end
+
+    # If modelmaker argument is provided, use that function to create models
+    # Otherwise, obtaine driver models using particle filtering
+    models = Dict{Int64,DriverModel}()
+    if isnothing(modelmaker)
+        print("Let's run particle filtering to create driver models")
+        models, = obtain_driver_models(f,id_list,50,ts,ts+50)
+    else
+        print("Lets use idm or c_idm to create driver models\n")
+        models = modelmaker(scene_real)
+    end
+
+    nticks = Int(ceil(dur/f.timestep))
+    scene_list = simulate(scene_real,f.roadway,models,nticks,f.timestep)
+
+    c_array = test_collision(scene_list,id_list)
+
+    truth_list = f.traj[ts:ts+nticks]
+
+    # Make a comparison video if filename provided
+    if !isempty(filename)
+        video_overlay_scenelists(scene_list,truth_list,id_list=id_list,roadway=f.roadway,
+            filename=filename)
+    end
+
+    # rmse dict with vehicle wise rmse values
+    rmse_pos_dict,rmse_vel_dict = compute_rmse(truth_list,scene_list,id_list=id_list)
+
+    # Average over the vehicles
+    rmse_pos = rmse_dict2mean(rmse_pos_dict)
+    rmse_vel = rmse_dict2mean(rmse_vel_dict)
+
+    return rmse_pos,rmse_vel,c_array
+end
+
+"""
+function pgfplot_vector(vec;leg = "nolegend")
+
+- Plot a vector using PGFPlots
+
+# Example
+```julia
+plot_rmse_pf = pgfplot_vector(pos_pf,leg="pf")
+```
+"""
+function pgfplot_vector(vec;leg = "nolegend")
+    return PGFPlots.Plots.Linear(collect(1:length(vec)),vec,legendentry=leg)
+end