AI Perception and Planning for Autonomous Driving

AI Perception and Planning for Autonomous Driving

Perception + Planning — it's the combination that turns sensor data flow into vehicle control commands. We, a team of engineers with 10+ years in Computer Vision and Robotics, solve this problem systemically: from sensor calibration to deployment on the onboard computer. With over 10 years of experience and 50+ successful projects, we deliver robust perception systems. The challenge of domain gap between simulation and reality is the main difficulty: even with mAP >0.9 on benchmark datasets, the system may lose an object on wet asphalt or incorrectly estimate a pedestrian's trajectory. Domain randomization, described in Wikipedia article on domain randomization, is a key technique. Without its application, accuracy drops 15–25% on real data, and fixing errors at the validation stage costs hundreds of thousands of dollars.

Methods to Bridge Simulation-to-Reality Gap

Domain randomization — key technique: we randomly vary textures, lighting, and weather in the simulator (CARLA, SUMO). Real2Sim — transfer real scenes to virtual environment via NeRF. Curriculum learning: first simple scenes, then corner cases. Without this, the model loses 15–25% mAP on real data. According to our estimates, proper application of domain randomization reduces errors by 25%, saving a significant amount at the validation stage.

Why Correct Detection Model Selection Matters

BEVFusion outperforms SECOND by 8 points in mAP but requires 3x more computational resources. For the onboard NVIDIA Drive Orin (128 TOPS), we use TensorRT-optimized BEVFusion at 100ms, achieving 2x speedup over standard implementation. In complex scenarios, we escalate to UniAD with reduced FPS. We validate models on open datasets such as nuScenes.

How We Calibrate Sensors

Calibration is the first and critical step. We use target-based method for LiDAR-camera fusion: set up a chessboard, collect 3D point-to-pixel correspondences, solve Perspective-n-Point problem. Accuracy — up to 0.1° in angle and 1 cm in translation. Calibration is repeated after every sensor disassembly.

Sensor Stack and Fusion

Autonomous systems of level L3+ work with multiple sensor types simultaneously:

import numpy as np
import torch
from mmdet3d.models import build_detector
from mmdet3d.apis import inference_detector

class PerceptionPipeline:
    def __init__(self, config: dict):
        # 3D detector: BEVFusion or SECOND
        self.detector_3d = build_detector(config['detector_cfg'])
        self.detector_3d.load_checkpoint(config['checkpoint'])

        # 2D camera detector: YOLOv8 or DETR
        self.cam_detector = torch.hub.load('ultralytics/ultralytics',
                                            'yolov8l', pretrained=True)

        # LiDAR → camera projection matrices
        self.lidar2cam = np.array(config['lidar2cam_matrix'])
        self.camera_intrinsics = np.array(config['cam_intrinsics'])

    def fuse_lidar_camera(self, point_cloud: np.ndarray,
                           images: list[np.ndarray]) -> dict:
        """
        LiDAR gives accurate 3D coordinates and range,
        camera gives semantics (object type, traffic light color).
        BEVFusion combines into a single Bird's Eye View representation.
        """
        bev_features = self._to_bev(point_cloud)
        cam_features = [self.cam_detector(img) for img in images]

        # Project LiDAR points onto camera plane
        pts_3d_cam = self._project_lidar_to_cam(point_cloud)

        return {
            'bev_features': bev_features,
            'cam_detections': cam_features,
            'projected_points': pts_3d_cam
        }

Planning: From Perception to Trajectory

class MotionPlanner:
    def __init__(self, config: dict):
        self.dt = 0.1  # time step 100ms
        self.horizon = 5.0  # planning horizon 5 sec
        self.safety_margin = 0.8  # meters

    def plan_trajectory(self, ego_state: dict,
                         detected_objects: list[dict],
                         hd_map: dict) -> np.ndarray:
        """
        IDM (Intelligent Driver Model) + potential fields.
        For complex scenarios: RL or transformer (PDM-Closed).
        """
        # Candidate trajectories from generator
        candidates = self._generate_candidates(ego_state)

        # Assess safety of each trajectory
        scores = []
        for traj in candidates:
            collision_risk = self._collision_check(traj, detected_objects)
            lane_keep = self._lane_keep_cost(traj, hd_map)
            comfort = self._comfort_cost(traj)

            total_cost = (3.0 * collision_risk +
                          1.5 * lane_keep +
                          0.5 * comfort)
            scores.append(total_cost)

        best_idx = np.argmin(scores)
        return candidates[best_idx]

    def _collision_check(self, trajectory: np.ndarray,
                          objects: list[dict]) -> float:
        """TTC (Time-To-Collision) for each object"""
        min_ttc = float('inf')
        for obj in objects:
            ttc = self._compute_ttc(trajectory, obj)
            min_ttc = min(min_ttc, ttc)

        # TTC < 2 sec = high risk
        return 1.0 / max(min_ttc, 0.1)

Work Process

1. Analytics and calibration — on-site visit, data collection from the vehicle, sensor calibration (LiDAR-camera).
2. Architecture design — model selection, define fusion pipeline, set latency and precision requirements.
3. Pipeline implementation — write detection, tracking, prediction, and planning code. Integrate with simulator.
4. Testing — A/B tests on open datasets (nuScenes, Waymo), validation on collected data, stress tests for corner cases.
5. Onboard deployment — optimization via TensorRT, ONNX Runtime, inference on target platform (NVIDIA Orin/Thor).

Approximate Timelines

Cost is calculated individually after analyzing your data and requirements. Typical budgets start at $50,000 for L2 ADAS.

What's Included

• Sensor calibration and reference data collection
• Development of perception pipeline (detection, tracking, prediction)
• Model training and adaptation for your scenarios
• Integration with planning and control
• Architecture and API documentation
• Training your team to work with the system

Common Mistakes and Our Experience

A frequent issue is overfitting to a specific scenario. We guarantee generalization through domain randomization and tests on independent data. Our certified specialists have 50+ completed projects in autonomous driving. For example, a recent L3 system based on BEVFusion and IDM planner: in 18 months achieved mAP 70.5 on nuScenes with 110ms latency on Orin, and calibration automation reduced time by 40%.

Get a commercial proposal with a detailed work plan — contact us to evaluate your project. We will offer a turnkey solution considering your timeline and budget. Order a consultation to discuss details.