Emergency Braking Intensity Prediction

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2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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Latest papers 13

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Closed-Loop Refinement and Execution for Learned Driving Planners
Oct 1, 2026Huaijin Hu, Shanting Wang, Zhongyu Mo +1Autonomous Driving Simulation Closed Loop
Systems Engineering Program, Cornell University, Ithaca, NY 14853 USA
Adaptive Safety Filtering for Frozen ACC Policies via Conformal Residual Calibration
Sep 28, 2026Zhiruo Zhou, Rigaudiere Z. Li, Chen Xiwen +3Safety Filters Residual Policy
Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China · Wuhan University of Technology, Wuhan, China · Shanghai Jiao Tong University, Shanghai, China
Combining Evasive and Braking Reactions for Safety Reference Models in Automated Vehicles
Sep 24, 2026Riccardo Donà, Konstantinos Mattas, Biagio CiuffoEmergency Braking Intensity Prediction Automated Vehicles
Joint Research Centre for the European Commission, Ispra, IT
CALM: Configuration-Aware Human Intervention Boundaries During Robot Approach
Sep 7, 2026Xinting Gao, Sipu Zhu, Weimin ZhuangEmergency Braking Intensity Prediction Human Agency
School of Architecture, Tsinghua University, China · Architectural Design & Research Institute of Tsinghua University, China
Drifting in the Future: Stabilizing Path Following Drifting on High-Latency Vehicle Systems
Jun 26, 2026Frederik Werner, Till Heintzenberg, Markus Lienkamp +1Vehicle Dynamics Emergency Braking Intensity Prediction
Professorship of Autonomous Vehicle Systems, TUM School of Engineering and Design, Technical University of Munich, 85748 Garching, Germany; Munich Institute of Robotics and Machine Intelligence (MIRMI)
Radar Guided Camera Verification for Automatic Emergency Braking Rethinking Object Detection in Radar Camera Fusion
Jun 25, 2026Ram Charan Akula, Sivanathan Kandhasamy, Manikandan GanesanRadar Emergency Braking Intensity Prediction
Autonomous Systems Lab, Department of Mechatronics Engineering, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India · Centre for Electric Mobility, Department of EEE, SRM Institute of Science and Technology, Kattankulathur, Chennai 603203, India
Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
Jun 17, 2026Mengxiang Hao, Xin Jiang, Xinghao Huang +10Emergency Braking Intensity Prediction Noisy Labels
All authors are with Li Auto, China.
Scaling Learning-based AEB with Massive Unlabeled Data
Jun 17, 2026Xiangyu Wang, Yang Zhan, Mengxiang Hao +9Emergency Braking Intensity Prediction Unlabeled Data
All authors are with Li Auto.
Reinterpreting Safety Thresholds as Neuron Spiking Thresholds
May 18, 2026Enrico Del Re, Mohamed Sabry, Cristina Olaverri-MonrealEmergency Braking Intensity Prediction Spiking Neural Networks
Johannes Kepler University Linz, Austria; Department Intelligent Transport Systems
Break the Brake, Not the Wheel: Untargeted Jailbreak via Entropy Maximization
May 11, 2026Mengqi He, Xinyu Tian, Xin Shen +6Jailbreak Attacks Jailbreak Success Rates
Australian National University, ACT, Australia · The University Of Queensland, Brisbane, Queensland, Australia · GE research, Niskayuna, New York, USA +2
Can You Break RLVER? Probing Adversarial Robustness of RL-Trained Empathetic Agents
May 8, 2026Deeraj S K, Sadhana Devarajan, Krishna Mehra +1Empathy Reinforcement Learning With Verifiable Reward
Department of Artificial Intelligence, Sardar Vallabhbhai National Institute of Technology, Surat, India
Real-Time GPU-Accelerated Monte Carlo Evaluation of Safety-Critical AEB Systems Under Uncertainty
Apr 29, 2026Akshay Karjol, Shadi AlawnehEmergency Braking Intensity Prediction Vehicle Dynamics
Department of Electrical and Computer Engineering, Oakland University, Rochester, MI 48309 USA
EEG-Based Emergency Braking Intensity Prediction Using Blind Source Separation
Apr 20, 2026Zikun Zhou, Wenshuo Wang, Wenzhuo Liu +5Electroencephalography Emergency Braking Intensity Prediction
School of Mechanical Engineering, Beijing Institute of Technology, Beijing, China

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Closed-Loop Refinement and Execution for Learned Driving Planners

Huaijin Hu, Shanting Wang, Zhongyu Mo, Andreas A. Malikopoulos

Systems Engineering Program, Cornell University, Ithaca, NY 14853 USA

Autonomous Driving Simulation Closed Loop Emergency Braking Intensity Prediction Steering Speeds

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Learning-based driving planners are usually trained and evaluated in open loop against logged trajectories. In closed loop, a trajectory with small displacement error can still stall the vehicle, steer it into a conflict with surrounding agents, or be executed with abrupt braking. We introduce Closed-Loop Refinement and Execution (CLRE), a hierarchical receding-horizon control framework designed to mitigate these failure modes while leaving the upstream planner frozen and adding no new learned model. The upper layer treats the nominal trajectory as a reference and solves a finite-horizon optimal control problem that trades route progress against interaction with predicted agents. Solving it from several initializations gives a candidate set, and a prediction-conditioned oriented-bounding-box (OBB) feasibility test retains only candidates whose minimum predicted OBB clearance over the horizon meets a threshold. The lower layer executes the lowest-cost survivor, or a route-centerline backup when none remains, through the tracking controller supplied with the planner, augmented by a range-based speed bound and a saturated proportional braking law. In closed-loop simulation on 126 Bench2Drive routes with VAD as the upstream planner, CLRE raises the driving score from 43.41 to 56.42 and route completion from 57.27 to 72.23, and reduces collision events from 70 to 53.

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Latest papers 13