Add CAN-driven lazy inference with resident TensorRT engines

include/lazy_gate.hpp

@@ -0,0 +1,36 @@
+#pragma once
+#include <array>
+#include <cstdint>
+#include <string>
+
+namespace cms {
+struct LazyGateConfig {
+    int threshold_percent{15};
+    int can_timeout_ms{1000};
+    int motion_timeout_ms{300};
+};
+struct InferenceStatus {
+    bool models_ready{};
+    bool enabled{true};
+    bool can_valid{};
+    bool left_front_trigger{},left_rear_trigger{};
+    std::array<int,4> positions{{-1,-1,-1,-1}}; // LF, RF, LR, RR; percent
+    std::array<int,2> direction{}; // LF, LR: -1 closing, 0 stationary, +1 opening
+    std::uint64_t generation{1},sample_monotonic_ns{};
+    double can_age_ms{-1};
+    std::string reason{"CAN_UNAVAILABLE"};
+};
+// Pure state machine. Caller serializes access. Time is monotonic nanoseconds.
+class LazyGate {
+public:
+    explicit LazyGate(LazyGateConfig config={});
+    void receive(std::uint32_t id,std::uint8_t const* data,std::size_t size,std::uint64_t now);
+    InferenceStatus snapshot(std::uint64_t now);
+private:
+    LazyGateConfig config_;
+    bool have_sample_{};
+    std::uint64_t last_sample_{};
+    std::array<std::uint64_t,2> last_change_{};
+    InferenceStatus state_;
+};
+}