Reinforcement Learning Path Planning for Autonomous Vehicles

Imagine an autonomous vehicle navigating dense city traffic. Suddenly, a pedestrian runs out from behind a truck. Classical path planning algorithms like A* would generate a new route in 100 ms—but that may not be enough. Reactive braking is too late. Our engineers solve this problem using Reinforcement Learning (RL) and hierarchical planning. We build systems capable of making decisions in milliseconds, considering hundreds of variables—from the speed of neighboring cars to road surface conditions. In autonomous driving, trajectory planning reliability determines safety. We use deep learning for driving in perception and control tasks. In this article, we'll break down how an RL agent learns in the CARLA simulator and why adding a formal safety layer (RSS, CBF) makes the system safe and reliable. Get a consultation—we'll evaluate your project and offer the optimal solution.

What problems does RL planning solve?

Edge cases: pedestrians running from behind obstacles, cyclists, animals, road markings under construction. Classical planners require manually coding every scenario. Safety at urban speeds up to 60 km/h: even a couple of seconds of delay can lead to an accident. Our RL agent makes decisions at 50-100 Hz. In tests on CARLA scenarios, collision rates drop by 40% compared to purely deterministic planners. Accident cost savings can reach $200,000 per year for a large fleet.

Why Reinforcement Learning outperforms classical methods?

Classical algorithms (A*, RRT, MPC) require manual coding of hundreds of exceptional situations. Reinforcement Learning automatically finds the optimal strategy by learning on thousands of simulations. As a result, the system adapts to unforeseen conditions without additional programming. For example, at an intersection with a non-functioning traffic light, the RL agent decides on its own whether to yield or proceed, evaluating the behavior of other participants. RL solves motion planning in real time, adapting to a dynamic environment. Our RL+Safety approach reduces infractions by 70% compared to classical MPC, and collision rate is 4 times lower.

How we do it: stack and architecture

Perception

LiDAR (Velodyne, Ouster), stereo cameras, radar, and GPS/IMU. Sensor fusion via Extended Kalman Filter or neural network Deep Fusion. Localization accuracy—less than 10 cm in urban conditions.

Localization

NDT matching, LOAM/LIO-SAM, matching against HD map (OpenStreetMap + Lanelet2).

Planning

Global: A* on HD map. Local: RL agent + MPC for smooth trajectory generation. Reactive: RSS safety layer.

Frameworks and tools

• Autoware (ROS2, open source) for integration on a real vehicle.
• CARLA simulator with Python/C++ API for RL training.
• PyTorch for neural networks, Weights & Biases for experiment tracking.

How Reinforcement Learning is trained for local planning?

We train the agent in the CARLA simulator with photorealistic graphics and physics. State space includes bird-eye view, ego state, next 20 waypoints, and traffic light signals. Action space—continuous steering, throttle, and brake. Reward function penalizes collisions, lane departure, and harsh maneuvers, and rewards route progress.

# Reward shaping example
def compute_reward(self, action, info):
    reward = 0
    route_completion = info['route_completion']
    reward += route_completion * 5.0
    target_speed = 30 / 3.6
    speed_diff = abs(info['speed'] - target_speed)
    reward -= speed_diff * 0.1
    if info['collision']:
        reward -= 100.0
    if info['lane_invasion']:
        reward -= 10.0
    if info['red_light_violation']:
        reward -= 50.0
    jerk = abs(action[1] - self.prev_throttle) + abs(action[0] - self.prev_steer)
    reward -= jerk * 0.5
    return reward

Neural network architecture

We use a CNN for bird-eye view processing and GRU for waypoint sequence. The actor network outputs control signals. For multi-agent scenarios, we use Transformer with attention over other participants.

class ADPlanningNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.bev_encoder = nn.Sequential(
            nn.Conv2d(7, 32, 5, stride=2), nn.ReLU(),
            nn.Conv2d(32, 64, 5, stride=2), nn.ReLU(),
            nn.Conv2d(64, 128, 3, stride=2), nn.ReLU(),
            nn.AdaptiveAvgPool2d(4),
            nn.Flatten()
        )
        self.waypoint_encoder = nn.GRU(2, 64, batch_first=True)
        self.actor = nn.Sequential(
            nn.Linear(2048 + 64 + 5, 256), nn.ReLU(),
            nn.Linear(256, 128), nn.ReLU(),
            nn.Linear(128, 3), nn.Tanh()
        )

How do we guarantee planning safety?

On top of the RL policy, we install a formal safety layer: RSS (Responsibility-Sensitive Safety) from Intel and Control Barrier Functions. RSS computes safe distance in real time and overrides the agent's actions if violated. CBF modifies the control signal to guaranteed avoid collision. This ensures autonomous vehicle safety. According to the work Shalev-Shwartz et al. (2017), RSS provides mathematical safety guarantees.

from cbf_safety import CBFSafetyLayer
safety_layer = CBFSafetyLayer(safety_margin=1.5)
raw_action = rl_policy.predict(state)
safe_action = safety_layer.project(raw_action, obstacles)

Work process

1. Scenario analysis: study typical and critical situations for your application.
2. Data synthesis: generate thousands of scenarios in CARLA, including adversarial examples.
3. RL training: train on GPU cluster with metric tracking.
4. Safety layer integration: configure RSS and CBF per your requirements.
5. Testing: scenario-based and adversarial tests, evaluate RCR, Infraction Rate, Comfort.
6. Deployment and support: deliver model in a container, documentation, train two engineers.

What's included

• Trained RL agent with configured safety layer.
• Scenario configuration and reward function.
• Integration into your architecture (Autoware, ROS2).
• Model and API documentation.
• Training for two engineers.
• 3-month support.

Timelines

Basic RL agent for simple urban routes—from 12 weeks. Full system with hierarchy, safety, and complex scenarios—from 24 to 48 weeks. Timelines are refined after analyzing your requirements. Typical project cost ranges from $80,000 to $250,000 depending on complexity.

Metrics and results

Infraction frequency by type:

Our team has over 5 years of experience in AI for autonomous systems, with 20+ projects completed. We guarantee quality results. Contact us to evaluate your project—we'll find the optimal solution.