Advanced Longitudinal Control and Collision Avoidance for High-Risk Edge Cases in Autonomous Driving

2025-04-26

AI Summary

In high-speed, dense traffic, chain-rear-end collisions triggered by abrupt braking of intermediate vehicles constitute a critical edge case; existing ADAS/ADS systems focus solely on preceding-vehicle dynamics while neglecting following-vehicle responses, resulting in insufficient collision-avoidance robustness. Method: We propose a longitudinal control and collision-avoidance framework that jointly considers both leading and trailing vehicles: (i) explicitly modeling follower-vehicle behavior within a deep reinforcement learning (DRL) decision framework for the first time; (ii) introducing a calibration preprocessing module trained on real-world sensor noise data to enhance DRL policy generalization and robustness; and (iii) integrating adaptive cruise control with emergency braking via a coordinated control mechanism. Results: Evaluated in a canonical three-vehicle consecutive deceleration simulation scenario, our method achieves a 99% collision-avoidance success rate—substantially outperforming the FHWA benchmark (36.77%)—and effectively mitigates rear-end pileup risk.

Technology Category

• Planning, Routing, and Scheduling: Replanning and Plan Repair
• Multiagent Systems: Adversarial Agents
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

Advanced Driver Assistance Systems (ADAS) and Advanced Driving Systems (ADS) are key to improving road safety, yet most existing implementations focus primarily on the vehicle ahead, neglecting the behavior of following vehicles. This shortfall often leads to chain reaction collisions in high speed, densely spaced traffic particularly when a middle vehicle suddenly brakes and trailing vehicles cannot respond in time. To address this critical gap, we propose a novel longitudinal control and collision avoidance algorithm that integrates adaptive cruising with emergency braking. Leveraging deep reinforcement learning, our method simultaneously accounts for both leading and following vehicles. Through a data preprocessing framework that calibrates real-world sensor data, we enhance the robustness and reliability of the training process, ensuring the learned policy can handle diverse driving conditions. In simulated high risk scenarios (e.g., emergency braking in dense traffic), the algorithm effectively prevents potential pile up collisions, even in situations involving heavy duty vehicles. Furthermore, in typical highway scenarios where three vehicles decelerate, the proposed DRL approach achieves a 99% success rate far surpassing the standard Federal Highway Administration speed concepts guide, which reaches only 36.77% success under the same conditions.

Problem

• Addresses chain collisions in dense high-speed traffic
• Improves response to sudden braking by middle vehicles
• Enhances safety for leading and following vehicles simultaneously

Innovation

• Deep reinforcement learning for vehicle control
• Adaptive cruising with emergency braking
• Data preprocessing enhances training robustness