@MAY Mobility:Autonomous Vehicle

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Why We Discuss this Topic; Because this Model uses May Mobility Behind the Development Process...

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Company: May Mobility

Establish year: May 2017

Co-Founder and CEO: Edwin Olson

Headquarters: Michigan; Japan

Level of autonomy: Level - 4

Approach Method: 4-pillar Method

Focus Industries: RoboTaxi

Sensors: 1- CAMERA'S

2- LIDAR'S

3- RADAR'S

They are Using Least Expensive Sensors

Key Features:

1- Multi-policy decision-making (MPDM) system that runs on the edge, primarily with CPUs instead of GPUs, requiring less computing power. (#USE CPU’S)

2- Low Latency = 200ms sec

Inside The Model

What is MPDM?

Imagine you're playing a game where you need to cross a busy playground filled with other kids running around. You can take different paths, like walking straight, zig-zagging, or waiting for a clear path. Each path has its risks and rewards. You quickly think about all the options and choose the one that feels the safest and fastest. That’s kind of like what MPDM does, but for autonomous vehicles!

What it Does: MPDM helps a self-driving car decide the best way to move safely and efficiently by considering multiple "what if" scenarios.
How it Works: The car doesn’t just pick one path at a time—it looks at many possible actions (like speeding up, slowing down, or turning) and simulates the outcomes for each choice.

The Simple Math Behind MPDM

Set of Policies:
Think of policies as different strategies or rules the car can follow.
Example:
- Policy A: "Drive straight and slow."
- Policy B: "Turn right carefully."
- Policy C: "Stop and wait."
Simulation of Scenarios:
The car uses its sensors and some probability math to simulate what could happen if it follows each policy. It looks at:
- How other cars might move.
- Whether a pedestrian might cross.
- What happens if the light turns red.
Evaluation:
For each policy, the car calculates a score using a formula like:
$\text{Score} = \text{Safety} + \text{Efficiency} - \text{Risk}$
Higher scores mean better options.
Choosing the Best Policy:
After evaluating all the policies, the car picks the one with the highest score.

An Example

Suppose an autonomous car is at a four-way intersection. It has three options:

Go straight.
Turn left.
Wait for 10 seconds.

The car uses MPDM to simulate:

What other vehicles might do for each choice.
How much time each option takes.
How safe each option is.

After simulating these scenarios, the car might decide that waiting 10 seconds is the safest and most reliable option based on the scores.

Why is MPDM Special?

Fast Decisions: MPDM can quickly simulate thousands of scenarios in real-time.
Works on CPUs: Unlike heavy computing systems, MPDM is designed to work efficiently on regular processors, making it cost-effective.
Handles Uncertainty: MPDM isn’t just about one answer—it keeps adjusting as new information comes in.

MPDM is like a super-smart brain for self-driving cars, constantly imagining "what could happen next" and picking the best move to stay safe and efficient.

List of Heuristic-driven MPDM algorithms:

Here’s a list of heuristic-driven algorithms and approaches commonly used in Multi-Policy Decision Making (MPDM) for autonomous vehicle development. These algorithms prioritize real-time decision-making efficiency and adaptability:

Heuristic-Driven MPDM Algorithms

Potential Fields (PF) Algorithms
- Use virtual attractive and repulsive forces to guide vehicles toward goals while avoiding obstacles.
- Example: Dynamic Window Approach (DWA) integrates PFs with kinematic constraints.
Rapidly-Exploring Random Tree (RRT) Variants
- Generate paths by exploring the state space randomly and evaluating their feasibility.
- Example:
  - RRT-Connect for fast pathfinding.
  - RRT* for optimal pathfinding.
  - Anytime RRT for time-constrained scenarios.
Behavior-Based Decision Making
- Policies are predefined for specific behaviors (e.g., follow, overtake, stop).
- Decisions are made by selecting the most appropriate behavior based on scenario-specific heuristics.
Velocity Obstacle (VO) Approaches
- Evaluate the vehicle's velocity space to determine safe trajectories.
- Example: Reciprocal Velocity Obstacles (RVO) account for multi-agent interactions dynamically.
Monte Carlo Tree Search (MCTS)
- A search-based heuristic that evaluates potential future states by simulating outcomes from the current state.
- Variants like UCT (Upper Confidence bounds applied to Trees) are common for balancing exploration and exploitation.
Finite State Machines (FSMs)
- Use a simple state-transition model where each state represents a specific driving behavior (e.g., cruising, stopping, turning).
- Policies are predefined for each state and transitions are determined heuristically.
Rule-Based Systems
- Define hardcoded rules for selecting policies under specific conditions.
- Example: Yielding to vehicles in an intersection based on predefined right-of-way rules.
Cost Maps and Gradient Descent
- Generate cost maps representing obstacles, goals, and other driving constraints.
- Policies are evaluated by minimizing the cost of trajectories.
Social Driving Heuristics
- Incorporate human-like heuristics to interact with other drivers predictively.
- Example: Gap acceptance for lane merges or yielding heuristics at intersections.
Game-Theoretic Heuristics
- Simplify multi-agent interactions into game-theoretic models where optimal policies are derived through heuristic approximations.
- Example: Nash Equilibrium or Stackelberg Games.
Simulated Annealing
- Evaluate a random set of policies and iteratively refine them by mimicking a cooling process.
- Useful for solving combinatorial decision-making problems.
Path Velocity Decomposition (PVD)
- Separates trajectory planning into path generation and velocity profiling, optimizing each step heuristically.
Multi-Agent Prediction-Based Heuristics
- Predict the behavior of surrounding agents (e.g., vehicles, pedestrians) and evaluate policies based on the likelihood of safe interactions.
- Example: Gaussian Process Regression for prediction heuristics.
Heuristic Cost-Based Policy Selection
- Assign weights to factors like safety, efficiency, and comfort, and select policies by minimizing a heuristic cost function.
Temporal Logic-Based Planning
- Use formal rules (e.g., "Always avoid obstacles") encoded as temporal logic to guide policy evaluation heuristically.
Priority Rules
- Simple prioritization heuristics, such as "Stop for pedestrians before crossing an intersection" or "Prioritize left-turn policies over straight-line movement in dense traffic."
Obstacle Avoidance Using Bounding Volume Hierarchies
- Represent obstacles using bounding volumes (e.g., spheres, boxes) and evaluate policies heuristically based on collision probabilities.

Advantages✌✌

Advantages of Multi-Policy Decision Making (MPDM)

1. Real-Time Adaptability

Advantage: MPDM evaluates multiple policies (plans of action) in real-time, allowing the system to adapt to rapidly changing environments.
Example: In a busy intersection, MPDM can dynamically choose between stopping, proceeding, or maneuvering around obstacles based on real-time sensor inputs.
Why It Matters: Traditional decision-making methods often rely on pre-computed plans, which might fail in dynamic situations. MPDM ensures continuous evaluation and adjustment.

2. Handles Uncertainty Effectively

Advantage: MPDM uses probabilistic models to account for uncertainties in the environment, such as unpredictable pedestrian behavior or vehicle movements.
Example: When a pedestrian appears to be hesitating at a crosswalk, MPDM can evaluate the likelihood of them crossing and adjust the vehicle’s behavior accordingly.
Why It Matters: Real-world scenarios are inherently unpredictable. MPDM’s probabilistic nature ensures safer and more robust decision-making under uncertainty.

3. Evaluation of Multiple Policies Simultaneously

Advantage: Instead of committing to one path and hoping for the best, MPDM evaluates several potential actions in parallel.
Example: At a highway exit, MPDM might simultaneously consider merging into different lanes, adjusting speed, or exiting later.
Why It Matters: By comparing multiple options, MPDM can choose the most optimal strategy for safety and efficiency.

4. Resource Efficiency

Advantage: MPDM is designed to work effectively with CPU-based systems, avoiding the need for expensive GPU hardware.
Example: Many other autonomous frameworks require GPUs for complex computations, increasing costs and power consumption. MPDM’s lightweight computation reduces these demands.
Why It Matters: This makes MPDM scalable and affordable for mass production, bringing autonomous technology to more vehicles and applications.

5. Predictive Decision-Making

Advantage: MPDM predicts the future actions of other agents (vehicles, pedestrians) and incorporates these predictions into its decision-making process.
Example: If another car is signaling to change lanes, MPDM can anticipate this movement and preemptively adjust its path to avoid conflict.
Why It Matters: Predictive capabilities enhance safety by preparing for potential risks before they occur.

6. Simplified Implementation

Advantage: MPDM’s modular structure makes it easier to integrate into existing autonomous systems compared to monolithic decision-making algorithms.
Example: A vehicle manufacturer can implement MPDM alongside existing perception and control systems with minimal adjustments.
Why It Matters: Lower integration complexity accelerates deployment and reduces development costs.

7. Scalability Across Scenarios

Advantage: MPDM can be applied across various driving environments, from urban streets to highways.
Example: In urban settings, MPDM can manage complex interactions with pedestrians and cyclists. On highways, it can optimize speed and lane changes.
Why It Matters: This flexibility makes MPDM a versatile solution for a wide range of autonomous vehicle applications.

8. Improved Safety

Advantage: By continuously simulating multiple "what-if" scenarios, MPDM ensures the chosen policy minimizes risks and avoids unsafe situations.
Example: In a scenario where a child runs onto the road, MPDM quickly evaluates braking, swerving, or stopping entirely to minimize harm.
Why It Matters: Safety is paramount in autonomous systems, and MPDM provides a robust framework for safer navigation.

9. Enhanced User Comfort

Advantage: MPDM ensures smoother rides by considering passenger comfort alongside safety and efficiency.
Example: Instead of harsh braking to avoid a hazard, MPDM might choose to decelerate more gradually if the situation allows.
Why It Matters: A comfortable ride experience increases user trust and acceptance of autonomous vehicles.

10. Independence from High-Definition Maps

Advantage: Unlike some systems that require highly detailed maps for navigation, MPDM relies on real-time sensor data, reducing dependency on pre-mapped environments.
Example: MPDM can operate effectively in areas where detailed maps are unavailable or outdated.

11. Cost-Effective Scalability

Advantage: By eliminating the need for high-performance GPUs and leveraging CPUs, MPDM reduces hardware costs while maintaining robust functionality.
Example: Affordable autonomous systems can be deployed in shared mobility services like shuttles and taxis, making them accessible to more communities.
Why It Matters: Cost efficiency is critical for scaling autonomous vehicle technology globally.

12. Alignment with Ethical Considerations

Advantage: MPDM allows for the incorporation of ethical priorities into its scoring mechanism (e.g., prioritizing human safety over speed).
Example: In a trolley-problem-like scenario, MPDM can weigh ethical outcomes when deciding between two risky actions.
Why It Matters: Ethical decision-making is essential for gaining public trust in autonomous systems.

Dis-Advantages

Disadvantages of Multi-Policy Decision Making (MPDM)

1. Computational Complexity

Challenge: MPDM involves simulating and evaluating multiple policies simultaneously, which can require significant computational resources, even if designed to work on CPUs.
Example: In highly complex environments like crowded urban intersections, the number of possible scenarios can grow exponentially, increasing the system’s computational burden.
Impact: This can lead to slower decision-making or require optimization techniques to ensure real-time performance.

2. Dependency on Accurate Sensor Data

Challenge: MPDM’s performance heavily relies on the accuracy and reliability of sensor inputs (e.g., lidar, cameras, radar).
Example: Poor weather conditions, like heavy rain or snow, can degrade sensor performance and lead to incorrect simulations.
Impact: This can result in suboptimal or unsafe decisions, undermining the effectiveness of the framework.

3. Limited Scalability to Extremely Dynamic Scenarios

Challenge: In environments where the situation changes faster than the system can evaluate policies, MPDM may struggle to keep up.
Example: In a high-speed highway merge with rapidly changing traffic patterns, MPDM might not evaluate scenarios quickly enough to make the safest decision.
Impact: Such delays can compromise safety or lead to overly conservative actions.

4. Trade-offs Between Safety and Efficiency

Challenge: MPDM often prioritizes safety, which can result in overly conservative decisions that affect efficiency.
Example: In low-risk situations, the vehicle might still choose to stop or slow down excessively, frustrating passengers and causing delays.
Impact: This could reduce user satisfaction and system adoption, particularly in competitive markets.

5. Ethical Decision-Making Limitations

Challenge: While MPDM can incorporate ethical considerations, defining and implementing these priorities in a universally accepted way is complex.
Example: In a “trolley problem” scenario, the system might struggle to balance conflicting ethical principles, such as prioritizing passenger safety over pedestrians.
Impact: Ethical dilemmas could lead to public skepticism and regulatory challenges.

6. Lack of Global Optimization

Challenge: MPDM focuses on real-time selection among predefined policies, which may not always align with globally optimal solutions over longer time horizons.
Example: A short-term policy might avoid a hazard but lead to a suboptimal route, increasing overall travel time.
Impact: This could reduce operational efficiency in scenarios requiring strategic, long-term planning.

7. Difficulties in Policy Predefinition

Challenge: MPDM relies on a set of predefined policies, and defining an exhaustive set of policies for all possible scenarios is challenging.
Example: A rare or highly unusual event, like an animal crossing the road in a high-speed scenario, might not be covered adequately by the existing policies.
Impact: Gaps in policy coverage can lead to unsafe or inefficient behavior.

8. Resource Constraints in Edge Deployments

Challenge: While MPDM is designed to operate on CPUs, its performance may still be limited by the computational power available in edge devices, especially in low-cost deployments.
Example: Autonomous shuttles deployed in resource-constrained environments might face difficulties maintaining real-time performance.
Impact: This can restrict the scalability of MPDM in cost-sensitive applications.

9. Training and Tuning Requirements

Challenge: Developing and fine-tuning the policies and evaluation mechanisms for MPDM requires significant effort and expertise.
Example: Each deployment environment (e.g., urban, highway) might require extensive reconfiguration and testing to ensure optimal performance.
Impact: This increases development time and costs, particularly for new or evolving environments.

10. Vulnerability to Edge Cases

Challenge: MPDM may struggle with rare or unexpected scenarios that fall outside its predefined policies.
Example: An unusual event, like a vehicle backing up on the highway, might not be handled effectively due to a lack of prior policy consideration.
Impact: These edge cases can lead to failures or accidents, raising safety concerns.

11. Potential for Overfitting Policies

Challenge: Policies optimized for specific conditions may not generalize well to new or changing environments.
Example: A policy designed for a particular city’s traffic patterns might not perform well in a different city with unique behaviors.
Impact: This limits the flexibility and transferability of MPDM across diverse settings.

12. Regulatory and Legal Challenges

Challenge: The complexity of MPDM’s decision-making process can make it difficult to explain or justify decisions in the event of an accident.
Example: If a collision occurs, it may be challenging to trace which policy was selected and why, complicating liability determination.
Impact: This could hinder regulatory approval and public acceptance.

Must Watchable Elements

1- May Mobility: Scaling Autonomous Vehicles in Japan with NTT and Toyota: Link

2- Multi-Policy Decision Making (MPDM) v. Early Commitment in Autonomous Driving: Link

3- Projects: 1- Link; 2- Link

4- MPDM: Multipolicy Decision-Making in Dynamic,Uncertain Environments for Autonomous Driving: Link

5- MAY Mobility Testing Results: Link

LAST WORDS:-
One thing to keep in mind is that AI and self-driving Car technologies are very vast...! Don't compare yourself to others, You can keep learning..........

Competition And Innovation Are Always happening...!
So you should be really comfortable with the change...

So keep slowly Learning step by step and implement, be motivated and persistent

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