diff --git a/starpilot/controls/lib/hybrid_experimental_mode.py b/starpilot/controls/lib/hybrid_experimental_mode.py
index 350c0c0a5..40475639d 100644
--- a/starpilot/controls/lib/hybrid_experimental_mode.py
+++ b/starpilot/controls/lib/hybrid_experimental_mode.py
@@ -21,14 +21,16 @@ def smooth_min(a: float, b: float, k: float = 6.0) -> float:
 def smooth_max(a: float, b: float, k: float = 6.0) -> float:
   return lerp(b, a, sigmoid(a - b, k=k))
 
+
 class HybridExperimentalMode:
   """
   Fuses Chill Mode (radar/lead tracking) and Experimental Mode (vision/stop signs/lights):
-  1. Detects vision braking intent from the E2E model trajectory.
-  2. Blends Chill and Exp acceleration smoothly based on intent.
-  3. Holds 0 m/s at standstills to prevent creep.
-  4. Falls back safely to Chill if lead vehicle distance is compromised.
-  5. Slew-rate limits acceleration to respect vehicle jerk limits.
+  1. Detects vision stopping intent from trajectory v_min and distance horizon.
+  2. Calculates kinematically required stopping deceleration (-v^2 / 2d).
+  3. Clamps Chill positive throttle during stop events to prevent brake fighting.
+  4. Holds 0 m/s at standstills to prevent creep.
+  5. Falls back safely to Chill if lead vehicle distance is compromised.
+  6. Slew-rate limits acceleration to respect vehicle jerk limits.
   """
 
   # Base physical actuator jerk limits (m/s^3)
@@ -45,8 +47,8 @@ class HybridExperimentalMode:
     self.exp_authority = 0.5
 
     # User tuning
-    self.HYBRID_EXP_BIAS = 0.0            # [-1.0, 1.0]
-    self.VISION_BRAKE_SENSITIVITY = 1.0   # [0.0, 2.0]
+    self.HYBRID_EXP_BIAS = 0.2            # [-1.0, 1.0]
+    self.VISION_BRAKE_SENSITIVITY = 1.2   # [0.0, 2.0]
 
     # Active profile parameters
     self.t_follow = self.BASE_T_FOLLOW
@@ -84,6 +86,14 @@ class HybridExperimentalMode:
       return np.array([v_ego], dtype=float)
     return np.asarray(traj_v, dtype=float)
 
+  @staticmethod
+  def _get_model_trajectory_x(model_v2) -> np.ndarray:
+    position = getattr(model_v2, "position", None)
+    traj_x = getattr(position, "x", None) if position is not None else None
+    if traj_x is None or len(traj_x) == 0:
+      return np.array([], dtype=float)
+    return np.asarray(traj_x, dtype=float)
+
   def update(self, v_ego, v_cruise, lead_one, model_v2, a_chill, a_exp,
              t_follow=None, jerk_factor=None):
     # 0. Sync profile parameters if passed per-frame
@@ -94,53 +104,65 @@ class HybridExperimentalMode:
     lead_status = bool(getattr(lead_one, "status", False))
     lead_d_rel = float(getattr(lead_one, "dRel", 150.0))
 
-    # 1. VISION INTENT DETECTION (How urgently does the vision model want to slow?)
+    # 1. VISION INTENT & STOP HORIZON DETECTION
     traj_v = self._get_model_trajectory_v(model_v2, v_ego)
-    v_terminal = float(traj_v[-1])
-    v_min = float(np.min(traj_v))
+    traj_x = self._get_model_trajectory_x(model_v2)
+
+    min_idx = int(np.argmin(traj_v))
+    v_min = float(traj_v[min_idx])
+    d_min = float(traj_x[min_idx]) if len(traj_x) > min_idx else 100.0
     v_ref = max(v_ego, 2.0)
 
-    speed_drop_ratio     = max(0.0, (v_ego - v_min) / v_ref)
-    stop_ahead_intent    = max(0.0, (v_ego - v_terminal) / v_ref) * sigmoid(v_ego, k=3.0, x0=1.0)
-    model_decel_strength = max(0.0, -a_exp / 3.0)
+    # Detect drop anywhere along trajectory (stop sign / red light profile)
+    speed_drop_ratio = max(0.0, (v_ego - v_min) / v_ref)
+    is_stopping_profile = sigmoid(1.5 - v_min, k=4.0, x0=0.0) * sigmoid(v_ego, k=3.0, x0=1.0)
+    model_decel_strength = max(0.0, -a_exp / 2.5)
 
-    raw_vision_metric = max(speed_drop_ratio, stop_ahead_intent, model_decel_strength)
+    raw_vision_metric = max(speed_drop_ratio, is_stopping_profile, model_decel_strength)
     w_vision = float(np.clip(raw_vision_metric * self.VISION_BRAKE_SENSITIVITY, 0.0, 1.0))
 
-    # Experimental authority weight (bias + vision confidence)
-    base_auth = 0.5 + (0.35 * self.HYBRID_EXP_BIAS)
-    alpha_exp = float(np.clip(base_auth + (0.5 * w_vision), 0.0, 1.0))
+    # Calculate actual kinematic braking required to stop at the detected stop line
+    if v_min < 1.2 and v_ego > 1.0 and d_min > 0.5:
+      # Kinematic decel: -v^2 / (2 * d) with 2.0m stop line cushion
+      d_stop_effective = max(d_min - 2.0, 1.5)
+      a_kinematic_stop = - (v_ego ** 2) / (2.0 * d_stop_effective)
+      a_exp_effective = min(a_exp, a_kinematic_stop)
+    else:
+      a_exp_effective = a_exp
+
+    # Dynamic Exp Authority: scales directly to 100% when vision intent is high
+    base_auth = float(np.clip(0.5 + (0.35 * self.HYBRID_EXP_BIAS), 0.0, 1.0))
+    alpha_exp = lerp(base_auth, 1.0, w_vision)
     self.exp_authority = alpha_exp
 
     # 2. ACCELERATION FUSION (Throttle vs Braking Regimes)
-    # Throttle: Be responsive (smooth_max), but prioritize stopping if vision sees a stop
+    # Throttle Regime: Snappy pickup on open roads
     a_throttle_optimal = smooth_max(a_chill, a_exp, k=4.0)
     a_throttle_conservative = smooth_min(a_chill, a_exp, k=4.0)
     a_throttle_fused = lerp(a_throttle_optimal, a_throttle_conservative, w_vision)
 
-    # Braking: Smoothly hand control to Exp based on vision confidence
-    a_brake_fused = lerp(a_chill, a_exp, alpha_exp)
+    # Braking Regime: CLAMP Chill to <= 0 so cruise throttle cannot fight the stop!
+    a_chill_brake = min(a_chill, 0.0)
+    a_brake_fused = lerp(a_chill_brake, a_exp_effective, alpha_exp)
 
-    # Pick between Accel and Brake regimes
+    # Regime Selection: If vision sees a stop (w_vision -> 1), FORCE braking regime (w_accel -> 0)
     phase_metric = smooth_min(a_chill, a_exp, k=4.0)
-    w_accel = sigmoid(phase_metric, k=3.0, x0=-0.1)
+    w_accel = sigmoid(phase_metric, k=3.0, x0=-0.1) * (1.0 - w_vision)
     a_fused = lerp(a_brake_fused, a_throttle_fused, w_accel)
 
     # 3. STANDSTILL ANCHOR (Prevent creeping at 0 mph)
-    is_stopped = sigmoid(0.3 - v_ego, k=8.0, x0=0.0)
-    is_terminal_stopped = sigmoid(0.8 - v_terminal, k=4.0, x0=0.0)
-    standstill_weight = is_stopped * is_terminal_stopped
+    is_stopped = sigmoid(0.4 - v_ego, k=8.0, x0=0.0)
+    is_min_stopped = sigmoid(0.8 - v_min, k=4.0, x0=0.0)
+    standstill_weight = is_stopped * is_min_stopped
     a_anchored = lerp(a_fused, smooth_min(a_fused, 0.0, k=8.0), standstill_weight)
 
     # 4. SAFETY BARRIER (Lead Vehicle Proximity Check)
     d_static_effective = self.D_STATIC_SAFE + max(0.0, 1.5 * (1.0 - (v_ego / 4.0)))
     d_safe = (v_ego * self.T_FOLLOW_SAFE) + d_static_effective
 
-    # Compute safety risk if lead is within minimum buffer
     distance_ratio = (lead_d_rel - d_static_effective) / max(d_safe - d_static_effective, 1.0)
     lead_safety_risk = sigmoid(1.0 - distance_ratio, k=5.0, x0=0.0) * float(lead_status)
 
-    # Fall back to Chill braking if Chill is more conservative
     a_emergency_brake = smooth_min(a_anchored, a_chill, k=6.0)
     a_safe = lerp(a_anchored, a_emergency_brake, lead_safety_risk)