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NEUAutonomousDriving408 · Sep 25, 2021 · d3092c9 · d3092c9
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Showing 6 changed files with 47 additions and 182 deletions.
diff --git a/control/control.py b/control/control.py
@@ -67,10 +67,9 @@ def lontitudeControlSpeed(speed, lonPid):
 
 
 def speedupJob(Controller, MyCar):
-    if MyCar.time >= 100:
-        Controller.speeduplimit = 98
-    # else:
-    #     Controller.speeduplimit = 70
+    if MyCar.time >= Controller.superspeeduplimittime \
+        and MyCar.overtakeSum != 0:
+        Controller.speeduplimit = Controller.superspeeduplimit
 
     Controller.speedPid.setSetpoint(Controller.speeduplimit)
     # 纵向控制 thorro_ and brake_

diff --git a/initial/initial.py b/initial/initial.py
@@ -25,6 +25,8 @@ class ControlData(object):
     def __init__(self):
 
         self.speeduplimit = 70
+        self.superspeeduplimit = 101    # super speedup max speed
+        self.superspeeduplimittime = 60 # super speedup time threshold
         self.followlimit = 40
         self.overtakelimit = 40
 

diff --git a/main.py b/main.py
@@ -13,7 +13,6 @@
 data["landLine"] = None
 data["radar"] = None
 data["image"] = None
-result = None
 distanceData = DistanceData()
 previous_distance =  DistanceData()
 current_distance = DistanceData()
@@ -53,7 +52,7 @@
         while True:
             distanceprocessing.run(distanceData, previous_distance, current_distance, MyCar)
 
-            print("current : ", distanceData.distance_mid, "left : ", distanceData.distance_left, "right : ", distanceData.distance_right)
+            # print("current : ", distanceData.distance_mid, "left : ", distanceData.distance_left, "right : ", distanceData.distance_right)
             # print("car decison : ", MyCar.cardecision)
 
             planning.run(distanceData, MyCar)

diff --git a/perception/DrivingDetection.py b/perception/DrivingDetection.py
@@ -45,7 +45,7 @@ def make_parser(left_num, right_num):
     parser.add_argument("-lb", "--leftbound", type=int, default=left_num)
     parser.add_argument("-rb", "--rightbound", type=int, default=right_num)
     parser.add_argument("-expn", "--experiment-name", type=str, default=None)
-    parser.add_argument("-n", "--name", type=str, default="yolox-x", help="model name")
+    parser.add_argument("-n", "--name", type=str, default="yolox-l", help="model name")
 
     parser.add_argument(
         "--path", default="./assets/dog.jpg", help="path to images or video"
@@ -62,11 +62,11 @@ def make_parser(left_num, right_num):
     parser.add_argument(
         "-f",
         "--exp_file",
-        default="./YOLOX/exps/default/yolox_x.py",
+        default="./YOLOX/exps/default/yolox_l.py",
         type=str,
         help="pls input your experiment description file",
     )
-    parser.add_argument("-c", "--ckpt", default=".//YOLOX/pretrainedmodel/yolox_x.pth", type=str, help="ckpt for eval")
+    parser.add_argument("-c", "--ckpt", default=".//YOLOX/pretrainedmodel/yolox_l.pth", type=str, help="ckpt for eval")
     parser.add_argument(
         "--device",
         default="gpu",
@@ -203,61 +203,7 @@ def visual(self, output, img_info, cls_conf=0.35):
 
         vis_res = vis(img, bboxes, scores, cls, cls_conf, self.cls_names)
         return vis_res
-
-
-def image_demo(predictor, vis_folder, path, current_time, save_result):
-    if os.path.isdir(path):
-        files = get_image_list(path)
-    else:
-        files = [path]
-    files.sort()
-    for image_name in files:
-        outputs, img_info = predictor.inference(image_name)
-        result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
-        print("shape: ", result_image.shape)
-        if save_result:
-            save_folder = os.path.join(
-                vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
-            )
-            os.makedirs(save_folder, exist_ok=True)
-            save_file_name = os.path.join(save_folder, os.path.basename(image_name))
-            logger.info("Saving detection result in {}".format(save_file_name))
-            cv2.imwrite(save_file_name, result_image)
-            # cv2.imshow("test", result_image)
-        ch = cv2.waitKey(0)
-        if ch == 27 or ch == ord("q") or ch == ord("Q"):
-            break
-
-
-def imageflow_demo(predictor, vis_folder, current_time, args):
-    cap = cv2.VideoCapture(args.path if args.demo == "video" else args.camid)
-    width = cap.get(cv2.CAP_PROP_FRAME_WIDTH)  # float
-    height = cap.get(cv2.CAP_PROP_FRAME_HEIGHT)  # float
-    fps = cap.get(cv2.CAP_PROP_FPS)
-    save_folder = os.path.join(
-        vis_folder, time.strftime("%Y_%m_%d_%H_%M_%S", current_time)
-    )
-    os.makedirs(save_folder, exist_ok=True)
-    if args.demo == "video":
-        save_path = os.path.join(save_folder, args.path.split("/")[-1])
-    else:
-        save_path = os.path.join(save_folder, "camera.mp4")
-    logger.info(f"video save_path is {save_path}")
-    vid_writer = cv2.VideoWriter(
-        save_path, cv2.VideoWriter_fourcc(*"mp4v"), fps, (int(width), int(height))
-    )
-    while True:
-        ret_val, frame = cap.read()
-        if ret_val:
-            outputs, img_info = predictor.inference(frame)
-            result_frame = predictor.visual(outputs[0], img_info, predictor.confthre)
-            if args.save_result:
-                vid_writer.write(result_frame)
-            ch = cv2.waitKey(1)
-            if ch == 27 or ch == ord("q") or ch == ord("Q"):
-                break
-        else:
-            break
+
 
 def driving_runtime(predictor, vis_folder, image, args, MyCar):
     # detection model inference 
@@ -345,15 +291,15 @@ def driving_runtime(predictor, vis_folder, image, args, MyCar):
                 point.y_ = outputs[0][i][3] / 1.33333
                 # when autonomous drving vehicle is in middle lane or in right lane,
                 # there will be a left bounding box to estimate distance.
-                if (MyCar.midlane == 0 or MyCar.midlane == -7) and \
+                if (MyCar.midlane == 0 or MyCar.midlane == -7.5) and \
                     MyCar.changing == False and \
                     triangle_left.isInTriangle(point): 
                     leftlist1.append(i)
                     leftlist2.append( (outputs[0][i][2] - outputs[0][i][0]) * (outputs[0][i][3] - outputs[0][i][1]) )
 
                 # when autonomous driving vehicle is in middle lane or left lane,
                 # there will be a right bounding box to estimate distance.
-                if (MyCar.midlane == 0 or MyCar.midlane == 7) and \
+                if (MyCar.midlane == 0 or MyCar.midlane == 7.5) and \
                     MyCar.changing == False and \
                     triangle_right.isInTriangle(point):
                     rightlist1.append(i)
@@ -445,7 +391,6 @@ def driving_runtime(predictor, vis_folder, image, args, MyCar):
     # visulize the bounding box into the original image            
     result_image = predictor.visual(outputs[0], img_info, predictor.confthre)
     img = cv2.cvtColor(result_image, cv2.COLOR_RGB2BGR)
-    # cv2.putText(img,"wei", (225, 100), 1, 2, (0,0,0))
 
     # visulize the two line of left bound and right bound
     templeft = args.leftbound // 1.3333
@@ -467,82 +412,3 @@ def driving_runtime(predictor, vis_folder, image, args, MyCar):
     cv2.waitKey(1)
 
     return distance_left,  distance_mid, distance_right
-
-
-def main(exp, args):
-    if not args.experiment_name:
-        args.experiment_name = exp.exp_name
-
-    file_name = os.path.join(exp.output_dir, args.experiment_name)
-    os.makedirs(file_name, exist_ok=True)
-
-    vis_folder = None
-    if args.save_result:
-        vis_folder = os.path.join(file_name, "vis_res")
-        os.makedirs(vis_folder, exist_ok=True)
-
-    if args.trt:
-        args.device = "gpu"
-
-    logger.info("Args: {}".format(args))
-
-    if args.conf is not None:
-        exp.test_conf = args.conf
-    if args.nms is not None:
-        exp.nmsthre = args.nms
-    if args.tsize is not None:
-        exp.test_size = (args.tsize, args.tsize)
-
-    model = exp.get_model()
-    logger.info("Model Summary: {}".format(get_model_info(model, exp.test_size)))
-
-    if args.device == "gpu":
-        model.cuda()
-        if args.fp16:
-            model.half()  # to FP16
-    model.eval()
-
-    if not args.trt:
-        if args.ckpt is None:
-            ckpt_file = os.path.join(file_name, "best_ckpt.pth")
-        else:
-            ckpt_file = args.ckpt
-        logger.info("loading checkpoint")
-        ckpt = torch.load(ckpt_file, map_location="cpu")
-        # load the model state dict
-        model.load_state_dict(ckpt["model"])
-        logger.info("loaded checkpoint done.")
-
-    if args.fuse:
-        logger.info("\tFusing model...")
-        model = fuse_model(model)
-
-    if args.trt:
-        assert not args.fuse, "TensorRT model is not support model fusing!"
-        trt_file = os.path.join(file_name, "model_trt.pth")
-        assert os.path.exists(
-            trt_file
-        ), "TensorRT model is not found!\n Run python3 tools/trt.py first!"
-        model.head.decode_in_inference = False
-        decoder = model.head.decode_outputs
-        logger.info("Using TensorRT to inference")
-    else:
-        trt_file = None
-        decoder = None
-
-    predictor = Predictor(model, exp, COCO_CLASSES, trt_file, decoder, args.device, args.fp16, args.legacy)
-    current_time = time.localtime()
-    if args.demo == "image":
-        image_demo(predictor, vis_folder, args.path, current_time, args.save_result)
-    elif args.demo == "video" or args.demo == "webcam":
-        imageflow_demo(predictor, vis_folder, current_time, args)
-    elif args.demo == "driving":
-        driving_runtime(predictor, vis_folder, image, args)
-
-
-
-if __name__ == "__main__":
-    args = make_parser().parse_args()
-    exp = get_exp(args.exp_file, args.name)
-
-    main(exp, args)
diff --git a/perception/perception.py b/perception/perception.py
@@ -30,7 +30,6 @@ def set_distance_right(self, dis):
 
 
 def convert_image_to_numpy_ndarray(imageframe_byte):
-
     return numpy.array(Image.open(imageframe_byte))
 
 
@@ -51,12 +50,12 @@ def run(perceptionFlag, data, PerceptionArgs, distanceData, MyCar):
                                                                         image=img, 
                                                                         args=PerceptionArgs["args"], 
                                                                         MyCar=MyCar)
-            if MyCar.midlane == 7:
+            if MyCar.midlane == 7.5:
                 distanceData.set_distance_left(float('inf'))
             else:
                 distanceData.set_distance_left(result_left.item())
 
-            if MyCar.midlane == -7:
+            if MyCar.midlane == -7.5:
                 distanceData.set_distance_right(float('inf'))
             else:
                 distanceData.set_distance_right(result_right.item())

diff --git a/planning/decision.py b/planning/decision.py
@@ -42,43 +42,43 @@ def run(distanceData, MyCar):
         findpath(distance, MyCar)  # left or right
         return
 
-    # stage 2-1
-    # 对于变道时左侧车卡位的情况 而且当前处于speedup阶段需要快速变道
-    # overtakesum 计数作弊
-    if(MyCar.cardecision == 'speedup'
-            and distance_mid > MyCar.saftydistance  # 远大于follow 12m条件
-            and distance_left < MyCar.saftydistance + 3 # 左侧车与前车很近
-            and MyCar.midlane == -7.5   # 当前在最右车道
-            and distance_mid - distance_left < MyCar.saftydistance
-            # and not MyCar.changing  # 保证超车只判断一次即可
-            # and MyCar.overtakeSum == 9
-            ):
-        # 超车完成后会自动回复到follow状态
-        MyCar.cardecision = 'overtake'
-        print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
-        # MyCar.midlane = 0
-        # findpath(distance, MyCar)  # left or right
-        MyCar.direction = 'left'
-        return
+    # # stage 2-1
+    # # 对于变道时左侧车卡位的情况 而且当前处于speedup阶段需要快速变道
+    # # overtakesum 计数作弊
+    # if(MyCar.cardecision == 'speedup'
+    #         and distance_mid > MyCar.saftydistance  # 远大于follow 12m条件
+    #         and distance_left < MyCar.saftydistance + 3 # 左侧车与前车很近
+    #         and MyCar.midlane == -7.5   # 当前在最右车道
+    #         and distance_mid - distance_left < MyCar.saftydistance
+    #         # and not MyCar.changing  # 保证超车只判断一次即可
+    #         # and MyCar.overtakeSum == 9
+    #         ):
+    #     # 超车完成后会自动回复到follow状态
+    #     MyCar.cardecision = 'overtake'
+    #     print("!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!")
+    #     # MyCar.midlane = 0
+    #     # findpath(distance, MyCar)  # left or right
+    #     MyCar.direction = 'left'
+    #     return
 
     # stage 2-2
     #对于变道时右侧车卡位的情况 而且当前处于speedup阶段需要快速变道
-    if(MyCar.cardecision == 'speedup'
-            and distance_mid > MyCar.saftydistance  # 远大于follow 12m条件
-            and distance_right < MyCar.saftydistance + 3 # 左侧车与前车很近
-            and MyCar.midlane == 7.5   # 当前在最zuo车道
-            and distance_mid - distance_right < MyCar.saftydistance
-            # and not MyCar.changing  # 保证超车只判断一次即可
-            # and MyCar.overtakeSum == 9
-            ):  #
+    # if(MyCar.cardecision == 'speedup'
+    #         and distance_mid > MyCar.saftydistance  # 远大于follow 12m条件
+    #         and distance_right < MyCar.saftydistance + 3 # 左侧车与前车很近
+    #         and MyCar.midlane == 7.5   # 当前在最zuo车道
+    #         and distance_mid - distance_right < MyCar.saftydistance
+    #         # and not MyCar.changing  # 保证超车只判断一次即可
+    #         # and MyCar.overtakeSum == 9
+    #         ):  #
 
-        # 超车完成后会自动回复到follow状态
-        MyCar.cardecision = 'overtake'
-        print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
-        # MyCar.midlane = 0
-        # findpath(distance, MyCar)  # left or right
-        MyCar.direction = 'right'
-        return
+    #     # 超车完成后会自动回复到follow状态
+    #     MyCar.cardecision = 'overtake'
+    #     print("@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@")
+    #     # MyCar.midlane = 0
+    #     # findpath(distance, MyCar)  # left or right
+    #     MyCar.direction = 'right'
+    #     return
 
     # stage 3
     # speedup and get close to the front car