St Germain en Laye, December 9th 2024.
The Prudence-Based Predictive ACC (Adaptive Cruise Control) system developed by NEXYAD uses a combination of Artificial Intelligence (Fuzzy Logic, and Possibility Theory) and Road Safety Expertise to provide a more reliable and cautious approach to predictive acceleration and braking in autonomous driving systems, specifically in Adaptive Cruise Control (ACC) systems.
Simulation Videos of Nexyad AI for Predictive ACC on Aurelion (dSPACE) :
Prudence-Based Approach:
Prudence in this context refers to a system that errs on the side of caution. Instead of optimizing for the most aggressive or efficient acceleration and deceleration, the system predicts and reacts in a way that prioritizes safety. The goal is to avoid risk by considering worst-case scenarios and uncertain conditions, such as unexpected road obstacles, weather changes, or other external factors.
Adaptive Cruise Control (ACC):
ACC systems adjust a vehicle’s speed to maintain a safe distance from the car in front. This is typically done by controlling the throttle and braking system. In NEXYAD’s approach, the system goes beyond simple speed regulation and incorporates predictive behavior using AI to anticipate changes in the road or traffic conditions, making it more responsive and adaptable to a variety of dynamic scenarios.
Artificial Intelligence (AI):
AI plays a central role in the system by processing vast amounts of data from sensors, cameras, and other vehicle systems in real time. The AI uses this data to predict the future state of the vehicle and surrounding environment, adjusting the ACC system accordingly. The more the AI learns, the more effectively it can predict and react to changing conditions.
- Fuzzy Logic: an approach to decision-making that mimics human reasoning and decision processes. Instead of relying on binary (true/false) logic, fuzzy logic allows for reasoning in terms of degrees of truth. In this case, fuzzy logic helps the system make decisions in situations where data may be imprecise or uncertain. For example, when determining the optimal distance from the car ahead, fuzzy logic can evaluate factors such as speed, weather conditions, and road quality in a more nuanced way than traditional binary systems.
Example: Instead of simply asking if the car ahead is too close (yes/no), the fuzzy system might evaluate how close the car is, how fast it’s going, how fast the vehicle is approaching, and other factors to make a more nuanced decision on acceleration or deceleration. - Possibility Theory: a mathematical framework used to handle uncertainty, especially when it comes to reasoning about vague or imprecise information. It is closely related to fuzzy logic, but while fuzzy logic deals with imprecise concepts and degrees of truth, possibility theory deals more with uncertainty in predicting future events or states.
In the context of NEXYAD’s system, possibility theory is used to evaluate and quantify the uncertainty in the system’s predictions. For example, when predicting the behavior of another vehicle or anticipating a potential obstacle, the system doesn’t just give one deterministic prediction, but rather a range of possible future scenarios with associated likelihoods. This allows the system to make more cautious and well-informed decisions, adjusting its actions based on the possibility of various outcomes.
Example: If the system predicts that an obstacle might appear on the road in the next few seconds, it considers the possibility that the obstacle may not appear, but it may still start decelerating, preparing for the worst-case scenario, which could involve an emergency stop.
Nexyad Road Safety expertise:
During more than 15 years, we were involved with 12 funded collaborative research programs, collecting knowledge of road safety experts, polices of the road, professional drivers and insurers of 19 countries. Nexyad interviewed and confronted hundreds of these experts to make them agree on situations and vocabulary. We understand why, when and where accidents happen and how avoid them.
How the System Works:
- Data Collection and Sensor Fusion: the system gathers data from multiple sensors such as electronic Horizon, radar, lidar, cameras, GPS, V2X, and vehicle control systems. This data is used to create a real-time model of the environment around the vehicle.
- Fuzzy Logic Decision-Making: based on the data, fuzzy logic rules are applied to evaluate various driving parameters, such as speed, distance to other vehicles, and road conditions. For example, a rule might state: « If the distance to the vehicle ahead is small AND the speed is high, THEN decelerate. »
- Predictive Modeling with Possibility Theory: using possibility theory, the system predicts future events or situations (e.g., the likelihood that the vehicle ahead will change lanes, that there will be an obstacle, or that road conditions will worsen). Instead of just assuming one scenario, the system models several possible futures and acts cautiously based on these possibilities. For example, it might slow down in anticipation of potential traffic changes, even if those changes are not certain.
- Prudence in Action: the system makes decisions based not just on what is most likely, but also what could happen in a worst-case scenario. This prudence-based behavior ensures that the vehicle can adapt in real time to sudden changes in the environment while ensuring safety by avoiding aggressive or risky maneuvers.
- Safe and Efficient Driving: the goal is to maintain smooth and comfortable driving while minimizing risks. The system balances the need for efficient travel with safety by predicting and reacting to potential dangers in a way that does not overreact but also does not under-react. It aims for an optimal balance where the vehicle’s behavior is safe and conservative yet responsive to traffic and environmental conditions.
Advantages of NEXYAD’s Prudence-Based ACC System:
- Improved Safety: By combining predictive AI with fuzzy logic and possibility theory, the system can anticipate potential dangers and adjust the vehicle’s behavior accordingly, improving safety and reducing the risk of accidents.
- Real-time Adaptability: The system continuously adapts to the dynamic conditions around the vehicle, reacting to changes in traffic, road conditions, and the behavior of other drivers.
- Efficient Handling of Uncertainty: Unlike traditional models, which might fail in ambiguous or uncertain situations, this system is designed to handle imprecision and uncertainty more effectively, making it more robust.
- Comfortable Driving Experience: Prudence-based decision-making ensures that the system does not engage in erratic or jerky acceleration/deceleration, leading to a smoother driving experience for passengers.
Applications:
- Autonomous Vehicles: The system can be integrated into fully autonomous vehicles for safe and efficient navigation.
- ADAS (Advanced Driver Assistance Systems): It can be used in ADAS to enhance safety features like collision avoidance, adaptive cruise control, and automatic emergency braking.
- Driver Assistance in Semi-Autonomous Vehicles: Even in semi-autonomous vehicles, where the driver must remain in control, this system can provide valuable assistance for handling complex, dynamic traffic situations.
NEXYAD’s Prudence-Based Predictive ACC system leverages AI, fuzzy logic, and possibility theory to create a sophisticated and cautious approach to adaptive cruise control. By accounting for uncertainty and prioritizing safety, the system offers a more reliable solution for autonomous and semi-autonomous driving, enhancing both the safety and comfort of the vehicle’s occupants.
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