A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle

Chaoyu Chen; Jiaqi Qu; Ziteng Gao

REVIEW 4 major objections 5 minor 24 references

A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle

T0 review · 4 major / 5 minor · reviewed 2026-08-05 · deepseek-v4-flash

Challenge this review Re-run · record.json Download PDF Read on arXiv ↗ Review JSON

Pith's one-line read Fusing sensor data, camera images, and predicted vehicle trajectories makes PPO lane-change decisions markedly safer in simulation.

desk verdict Useful integration of known components, but the headline claim of 'significantly' rests on a single seed with no error bars or statistical tests; needs a stats pass before it can be accepted. read the letter →

arxiv 2509.01611 v1 pith:PRT4IDKC submitted 2025-09-01 cs.RO

Ziteng Gao , Jiaqi Qu , Chaoyu Chen This is my paper · ORCID

classification cs.RO

keywords lanechangedecision-makingdeepreinforcementlearninghybridstatespacetrajectorypredictionPPOautonomousdrivingsensorfusionCARLA

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

The paper sets out to show that a lane-change decision agent becomes safer when it is given a hybrid state: current low-dimensional sensor data, visual features from a semantic image, and transformer-predicted future positions of the eight nearest vehicles. In CARLA simulations with dense autopilot traffic, the resulting Hybrid-PPO policy succeeds in 87.8% of episodes and collides in 10.5%, versus 47.6% and 50.0% for a sensor-only PPO baseline. The paper attributes this improvement to the richer state space, with ablations showing each added input—image, prediction, then both—monotonically improves success and reduces collisions. The broader claim is that the quality of the state representation, not just the RL algorithm, is what unlocks safe lane-changing in traffic flow.

What carries the argument

The central object is the hybrid state space, built from three parallel encodings: current low-dimensional sensor data, semantic-image features extracted by a VGG-16 convolutional network, and future coordinates of the eight nearest vehicles predicted by a transformer model that attends to their last ten positions. The agent is a Proximal Policy Optimization (PPO) policy with a discrete action space—left, stay, right—and a reward function that penalizes collisions and off-road events while rewarding progress and reaching the destination. The hybrid state is the mechanism carrying the argument: each ablation removes one component and performance degrades, which is how the paper attributes the

What would settle it

Retrain or re-evaluate Hybrid-PPO with the predicted-future branch disabled but with the same observation inputs, or inject controlled noise into the predicted coordinates; if the collision rate stays near 10.5% rather than degrading toward the prediction-free baseline, the paper's attribution of safety to trajectory prediction fails. A cheaper check is measuring whether prediction error (ADE/FDE) correlates with per-episode collision outcomes in the CARLA test episodes.

Watch

Extended reading notes

Core claim

On the paper's own terms, the central claim is that a deep reinforcement learning agent using a hybrid state space—composed of current low-dimensional sensor observations, VGG-16-extracted features from a top-down semantic image, and a transformer's predicted future positions of the eight nearest surrounding vehicles—makes safer lane-change decisions than any single information source. The paper tests this by training PPO agents on identical decision tasks with different state inputs in CARLA. The all-hybrid agent achieves 87.8% success and 10.5% collision rate, while the sensor-only agent achieves 47.6% and 50.0%; adding only images gives 63.8%/34.0% and adding only predictions gives 76.4%/

Load-bearing premise

The safety gain depends on the transformer's predicted future positions being accurate enough within the training environment; if they were frequently wrong, the agent would act on misleading information and the measured improvement could reverse.

Editorial extensions

If this is right

If the result transfers beyond the CARLA map it was trained on, lane-change modules could treat state-space composition as a first-order safety lever.
Fusing future trajectory predictions with visual context reduces collision rates in dense traffic: the paper's numbers drop from 50.0% (sensor-only) to 10.5% (hybrid) collision rate.
Prediction information alone improves safety more than images alone (76.4% vs 63.8% success), suggesting that modeling surrounding vehicles' future positions matters more than adding visual detail.
The hybrid-state design can be combined with any on-policy RL algorithm; the paper uses PPO, but the state representation is the claimed contribution.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

A natural extension the paper does not run: replace the transformer's predictions with random or corrupted coordinates and rerun the ablation; if safety stays at the hybrid level, the prediction branch is not the source of the gain.
The same hybrid state recipe could transfer to merging, overtaking, or intersection-crossing decisions, where future positions of neighbors are equally decision-relevant.
Because the trajectory predictor is trained on autopilot behavior rather than on the RL agent's own traffic, its errors under distribution shift are unmeasured; measuring how degradation in ADE/FDE alters collision rate would test robustness.
The paper's comparisons are against RL baselines under the same reward, so the improvement isolates the value of added information; how Hybrid-PPO compares with cost-function-based planners is left open.

Share X Bluesky LinkedIn Reddit HN

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

You should know: the paper is a competent engineering integration of established pieces—PPO for lane-change decisions, VGG-16 for image features, a transformer for predicting surrounding-vehicle trajectories, and PID for low-level control. The new bit is the specific hybrid state space and the measured ablation improvement in CARLA. That is a real contribution of the narrow kind: no paradigm shift, but a plausible way to combine modalities for a practical task. The ablations show a monotonic improvement as you add image and prediction information, which is at least internally consistent and suggests the hybrid state is doing something.

The soft spot is the evidence quality. The central claim in the abstract uses the word "significantly," but there are no repeated seeds, no error bars, no significance tests, and no statement of how many evaluation episodes were used. The gap between Hybrid-PPO (87.8% success) and Pure-PPO (47.6%) is large, but PPO training in CARLA is stochastic in initialization, sampling, and traffic spawns; without variance information, that gap could be run-to-run luck. The stress-test note is right to flag this. The fact that Table IV percentages don't sum to 100 (Hybrid 101.0%, Image-PPO 98.0%) also points to imprecise evaluation accounting. The trajectory-prediction reliability worry is real but secondary: Prediction-PPO already beats Pure-PPO, so even imperfect predictions provide useful signal. The bigger problem is that the headline claim is currently unverified.

The baselines are also weak—A2C and DQN from 2018, neither a strong representative of current DRL for lane changing. And no code or data is released, which makes independent checking harder. That said, the methods are standard, the writing is clear, and the authors do not overclaim beyond the simulator—they say "in the CARLA simulator" rather than claiming real-world transfer.

Who is this for? A researcher working on DRL-based driving decisions in simulators, who wants a concrete example of multimodal state fusion. It deserves a serious referee: the architecture is sensible, the ablation design is reasonable, and the missing statistics are fixable. I'd send it for review, but I would ask for multiple seeds, variance reporting, and stronger baselines before accepting it. I wouldn't cite it in my own work in the next year, but I'd keep it in mind as a data point if I were doing a survey of hybrid observation spaces.

Referee Report

4 major / 5 minor

Summary. The paper proposes Hybrid-PPO, a deep reinforcement learning method for lane-change decision-making in autonomous vehicles. The state space combines low-dimensional sensor data (eight nearest vehicles), VGG-16 features extracted from a 120x120 semantic image, and transformer-based predicted trajectories of surrounding vehicles. The action space is discrete (lane-change left/stay/right), and the decision is executed by a PID controller. The method is evaluated in CARLA with dense traffic, comparing against A2C and DQN baselines and ablating the state-space components. The authors claim that the hybrid state space significantly enhances lane-change safety, citing success and collision rates in Tables III and IV.

Significance. If the empirical claims are validated, the main contribution is an engineering integration of trajectory prediction, visual features, and low-dimensional sensor data into a PPO-based lane-change decision module. The approach is plausible and the ablation trend is qualitatively encouraging. However, the paper does not provide code, machine-checked proofs, or statistically grounded evidence; the central claim rests on a small set of single-run simulator experiments. The strengths are the clear problem decomposition and the use of standard, reproducible components (PPO, VGG-16, transformer, PID). The significance is moderate: the integration is useful but not conceptually novel, and the current evidence is insufficient to support the headline claim.

major comments (4)

[§V.D, §V.E, Tables III and IV] The central claim that the hybrid state space 'significantly enhances' safety is not statistically supported. No information is given about the number of evaluation episodes, number of training seeds, variance across runs, or significance tests. The large gaps (e.g., 87.8% vs. 47.6% success) could be run-to-run variance, especially since PPO and CARLA spawn dynamics are stochastic. Please report mean and standard deviation over multiple seeds, state the evaluation episode count, and run a significance test (e.g., paired bootstrap or Wilcoxon) for the comparison. This is load-bearing for the abstract's claim.
[§V.E, Table IV] The outcome percentages do not sum to 100%: Hybrid-PPO gives 87.8+10.5+2.7=101.0% and Image-PPO gives 63.8+34.0+0.2=98.0%. This suggests the three categories (success, collision, timeout) are not mutually exclusive or exhaustively accounted for, or there is a calculation error. Since these numbers are the entire quantitative support for the ablation claim, the evaluation protocol must be clarified and the table corrected.
[§IV.A, §V.C] The trajectory prediction module is trained on a small dataset (2000 steps at 20 Hz, i.e., 100 seconds) from autopilot-driven vehicles, and only standalone ADE/FDE metrics (0.382/0.597) are reported. The paper does not analyze how prediction errors propagate to decision safety in the RL environment. The ablation shows that adding prediction improves over Pure-PPO, which is helpful, but the safety benefit is attributed to prediction accuracy. Please include an analysis of sensitivity to prediction perturbations, e.g., evaluate Hybrid-PPO with noise added to predicted positions, or compare with ground-truth future positions vs. predicted positions.
[§V.D, Table III] The comparison baselines A2C [14] and DQN [15] are from 2018 and use different network architectures and input representations, so they do not isolate the effect of the proposed hybrid state space. The comparison is further weakened because the PPO algorithm is not held constant; a fairer baseline would be PPO with low-dimensional input only (which is the Pure-PPO ablation) and PPO with image or prediction input separately. The current Table III claims 'reliability' but only shows that Hybrid-PPO beats older algorithms, which is a weak and potentially misleading benchmark. Please either strengthen the baselines or reinterpret the results.

minor comments (5)

[§IV.A] Equation (4) is garbled: 'VGG-16 output feature size' text is corrupted, and the symbol mapping is unclear. Also, the sentence 'Trajectory prediction:' appears without a proper colon in the observation space list.
[§V.C, PID controller] The PID parameter listing says 'Kp = 1.95, Kp = 0.2, Kp = 0.07' — the second and third terms are presumably Ki and Kd. The same issue appears for the longitudinal parameters.
[§III.A] There is a duplicated fragment: 'γ is discount function. At each step of MDP, a RL agent in γ is discount function.' This should be cleaned up.
[§V.C, Evaluation metrics] The text defines 'MAD' and 'FAD' but these are nonstandard; the standard terms are ADE (Average Displacement Error) and FDE (Final Displacement Error). Also, 'Mean Average Displacement' should be corrected.
[§V.E, Figs. 5 and 6] The figures are referenced but not described in the text; please add a sentence explaining what is plotted in each figure and how to read the convergence curves.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity; the central claim rests on an independent ablation, not on fitted inputs or self-citation.

full rationale

The paper's central claim is that a hybrid state space—low-dimensional sensor data, VGG-16-extracted image features, and transformer-based trajectory predictions—improves lane-change safety (Abstract; Tables III and IV). The derivation chain is not circular. The trajectory prediction model is trained separately on a collected CARLA autopilot trajectory dataset (Section V.C) and evaluated with ADE/FDE, then used as a fixed input feature to the PPO policy; it is not fitted to the RL success or collision rates. The reward function (Eqs. 5-6) is hand-designed with fixed constants and does not encode the hybrid state's superiority. The ablation study (Table IV) compares PPO variants with and without each input modality under the same algorithm and reward, so the hybrid variant's higher success rate is an empirical outcome, not a constructed equivalence. There are no self-citations used as load-bearing support, and no 'uniqueness theorem' or ansatz is imported from prior work by the same authors. The absence of repeated-seed or error-bar reporting is a legitimate evidence-quality concern, but it concerns statistical verification, not circularity. Therefore the paper is self-contained with respect to the circularity criteria.

Assumptions & free parameters 4 free parameters · 5 assumptions · 0 invented entities

The central claim is an empirical comparison of state-space designs, so the main free parameters are hand-tuned reward and control gains plus missing hyperparameters. No invented entities (e.g., new particles, forces, or dimensions) are introduced.

free parameters (4)

Reward function weights = R_collide=-500, R_out_road=-10, R_go_forward=100/distance, R_success=1000
Hand-tuned in Eq. 6; no sensitivity analysis is provided, and these weights shape the optimal policy.
PID control gains = Lateral: Kp=1.95, Ki=0.2, Kd=0.07; Longitudinal: Kp=1, Ki=0, Kd=0.75
Hand-set in Section V.C; not learned, but they affect how lane-change decisions are executed.
PPO hyperparameters = Not reported
Learning rate, clip epsilon, batch size, epochs, and GAE parameters are absent, making training unreproducible.
VGG-16 feature output dimension = Matched to sensor data size
In Eq. 4, the output feature size is set equal to the low-dimensional sensor vector, a design choice without justification.

assumptions (5)

domain assumption The lane-change task is modeled as a discrete-action MDP (S,A,R,P,gamma)
Section III.A; simplifies the decision problem to three abstract actions.
standard math PPO with clipped surrogate objective converges to a good policy
Section III.B; PPO is standard but convergence to an optimal lane-change policy is not proven.
domain assumption Transformer self-attention can accurately predict vehicle trajectories from a 10-step history
Section IV.A; the transformer is trained and validated on a small CARLA dataset, not on diverse traffic scenarios.
domain assumption A 120x120 semantic image after VGG-16 retains task-relevant driving information
Section IV.A; no analysis of information loss from dimension reduction is provided.
domain assumption Simulation results in CARLA Town06 transfer to real driving
Section V.B; no real-world validation, so safety claims are limited to the simulator.

how reviews work

0 comments

Cite this review

Pith. "Pith review of A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle." pith.science (2026). https://pith.science/paper/PRT4IDKC

@misc{pith2026250901611,
  author       = {Pith},
  title        = {Pith review of: A Hybrid Input based Deep Reinforcement Learning for Lane Change Decision-Making of Autonomous Vehicle},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/PRT4IDKC}},
  note         = {Machine review of arXiv:2509.01611}
}

read the original abstract

Lane change decision-making for autonomous vehicles is a complex but high-reward behavior. In this paper, we propose a hybrid input based deep reinforcement learning (DRL) algorithm, which realizes abstract lane change decisions and lane change actions for autonomous vehicles within traffic flow. Firstly, a surrounding vehicles trajectory prediction method is proposed to reduce the risk of future behavior of surrounding vehicles to ego vehicle, and the prediction results are input into the reinforcement learning model as additional information. Secondly, to comprehensively leverage environmental information, the model extracts feature from high-dimensional images and low-dimensional sensor data simultaneously. The fusion of surrounding vehicle trajectory prediction and multi-modal information are used as state space of reinforcement learning to improve the rationality of lane change decision. Finally, we integrate reinforcement learning macro decisions with end-to-end vehicle control to achieve a holistic lane change process. Experiments were conducted within the CARLA simulator, and the results demonstrated that the utilization of a hybrid state space significantly enhances the safety of vehicle lane change decisions.

Discussion (0). Continue with ORCID to comment.

Reference graph

Works this paper leans on

24 extracted references · 23 canonical work pages

[14]

Motion planning among dynamic, decision -making agents with deep reinforcement learning,

M. Everett, Y . F. Chen, and J. P . How, "Motion planning among dynamic, decision -making agents with deep reinforcement learning," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018: IEEE, pp. 3052 - 3059

work page 2018
[15]

Automated speed and lane change decision making using deep reinforcement learning,

C.-J. Hoel, K. Wolff, and L. Laine, "Automated speed and lane change decision making using deep reinforcement learning," in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018: IEEE, pp. 2148-2155

work page 2018
[1]

Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,

Z. Wadud, D. MacKenzie, and P . Leiby, "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, vol. 86, pp. 1-18, 2016

work page 2016
[2]

Flow: A modular learning framework for mixed autonomy traffic,

C. Wu, A. R. Kreidieh, K. Parvate, E. Vinitsky, and A. M. Bayen, "Flow: A modular learning framework for mixed autonomy traffic," IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1270- 1286, 2021

work page 2021
[3]

Online prediction of lane change with a hierarchical learning -based approach,

X. Liao et al. , "Online prediction of lane change with a hierarchical learning -based approach," in 2022 International Conference on Robotics and Automation (ICRA), 2022: IEEE, pp. 948-954

work page 2022
[4]

Driver Monitoring -Based Lane -Change Prediction: A Personalized Federated Learning Framework,

R. Du, K. Han, R. Gupta, S. Chen, S. Labi, and Z. Wang, "Driver Monitoring -Based Lane -Change Prediction: A Personalized Federated Learning Framework," in 2023 IEEE Intelligent Vehicles Symposium (IV), 2023: IEEE, pp. 1-7

work page 2023
[5]

Lane change decision-making through deep reinforcement learning with rule - based constraints,

J. Wang, Q. Zhang, D. Zhao, and Y . Chen, "Lane change decision-making through deep reinforcement learning with rule - based constraints," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019: IEEE, pp. 1-6

work page 2019
[6]

Value -function approximations for partially observable Markov decision processes,

M. Hauskrecht, "Value -function approximations for partially observable Markov decision processes," Journal of artificial intelligence research, vol. 13, pp. 33-94, 2000

work page 2000

Show all 24 references

[7]

Deep learning sensor fusion for autonomous vehicle perception and localization: A review,

J. Fayyad, M. A. Jaradat, D. Gruyer, and H. Najjaran, "Deep learning sensor fusion for autonomous vehicle perception and localization: A review," Sensors, vol. 20, no. 15, p. 4220, 2020

2020
[8]

Reinforcement Learning with Probabilistically Safe Control Barrier Functions for Ramp Merging,

S. Udatha, Y . Lyu, and J. Dolan, "Reinforcement Learning with Probabilistically Safe Control Barrier Functions for Ramp Merging," in 2023 IEEE International Conference on Robotics and Automation (ICRA), 2023: IEEE, pp. 5625-5630

2023
[9]

Learning to drive in a day,

A. Kendall et al. , "Learning to drive in a day," in 2019 International Conference on Robotics and Automation (ICRA) , 2019: IEEE, pp. 8248-8254

2019
[10]

Driving in dense traffic with model -free reinforcement learning,

D. M. Saxena, S. Bae, A. Nakhaei, K. Fujimura, and M. Likhachev, "Driving in dense traffic with model -free reinforcement learning," in 2020 IEEE International Conference on Robotics and Automation (ICRA), 2020: IEEE, pp. 5385-5392

2020
[11]

Stochastic model -predictive control for lane change decision of automated driving vehicles,

J. Suh, H. Chae, and K. Yi, "Stochastic model -predictive control for lane change decision of automated driving vehicles," IEEE Transactions on Vehicular Technology, vol. 67, no. 6, pp. 4771 - 4782, 2018

2018
[12]

Scalable decision making with sensor occlusions for autonomous driving,

M. Bouton, A. Nakhaei, K. Fujimura, and M. J. Kochenderfer, "Scalable decision making with sensor occlusions for autonomous driving," in 2018 IEEE international conference on robotics and automation (ICRA), 2018: IEEE, pp. 2076-2081

2018
[13]

Virtual -to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,

L. Tai, G. Paolo, and M. Liu, "Virtual -to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation," in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2017: IEEE, pp. 31-36

2017
[16]

Deep learning -based vehicle behavior prediction for autonomous driving applications: A review,

S. Mozaffari, O. Y . Al -Jarrah, M. Dianati, P. Jennings, and A. Mouzakitis, "Deep learning -based vehicle behavior prediction for autonomous driving applications: A review," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 1, pp. 33-47, 2020

2020
[17]

A survey on motion prediction and risk assessment for intelligent vehicles,

S. Lefè vre, D. V asquez, and C. Laugier, "A survey on motion prediction and risk assessment for intelligent vehicles," ROBOMECH journal, vol. 1, no. 1, pp. 1-14, 2014

2014
[18]

A recurrent neural network solution for predicting driver intention at unsignalized intersections,

A. Zyner, S. Worrall, and E. Nebot, "A recurrent neural network solution for predicting driver intention at unsignalized intersections," IEEE Robotics and Automation Letters, vol. 3, no. 3, pp. 1759-1764, 2018

2018
[19]

Long short term memory for driver intent prediction,

A. Zyner, S. Worrall, J. Ward, and E. Nebot, "Long short term memory for driver intent prediction," in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017: IEEE, pp. 1484-1489

2017
[20]

Convolution neural network -based lane change intention prediction of surrounding vehicles for ACC,

D. Lee, Y . P . Kwon, S. McMains, and J. K. Hedrick, "Convolution neural network -based lane change intention prediction of surrounding vehicles for ACC," in 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), 2017: IEEE, pp. 1-6

2017
[21]

Multi -modal motion prediction with transformer-based neural network for autonomous driving,

Z. Huang, X. Mo, and C. Lv, "Multi -modal motion prediction with transformer-based neural network for autonomous driving," in 2022 International Conference on Robotics and Automation (ICRA), 2022: IEEE, pp. 2605-2611

2022
[22]

Proximal policy optimization algorithms,

J. Schulman, F. Wolski, P. Dhariwal, A. Radford, and O. Klimov, "Proximal policy optimization algorithms," arXiv preprint arXiv:1707.06347, 2017

2017 arXiv
[23]

CARLA: An open urban driving simulator,

A. Dosovitskiy, G. Ros, F. Codevilla, A. Lopez, and V . Koltun, "CARLA: An open urban driving simulator," in Conference on robot learning, 2017: PMLR, pp. 1-16

2017
[24]

Transformer networks for trajectory forecasting,

F. Giuliari, I. Hasan, M. Cristani, and F. Galasso, "Transformer networks for trajectory forecasting," in 2020 25th international conference on pattern recognition (ICPR) , 2021: IEEE, pp. 10335-10342

2020

Pith tools

Reviewed August 5, 2026 · model on record in the stance chip above.

[1] [14]

Motion planning among dynamic, decision -making agents with deep reinforcement learning,

M. Everett, Y . F. Chen, and J. P . How, "Motion planning among dynamic, decision -making agents with deep reinforcement learning," in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018: IEEE, pp. 3052 - 3059

work page 2018

[2] [15]

Automated speed and lane change decision making using deep reinforcement learning,

C.-J. Hoel, K. Wolff, and L. Laine, "Automated speed and lane change decision making using deep reinforcement learning," in 2018 21st International Conference on Intelligent Transportation Systems (ITSC), 2018: IEEE, pp. 2148-2155

work page 2018

[3] [1]

Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles,

Z. Wadud, D. MacKenzie, and P . Leiby, "Help or hindrance? The travel, energy and carbon impacts of highly automated vehicles," Transportation Research Part A: Policy and Practice, vol. 86, pp. 1-18, 2016

work page 2016

[4] [2]

Flow: A modular learning framework for mixed autonomy traffic,

C. Wu, A. R. Kreidieh, K. Parvate, E. Vinitsky, and A. M. Bayen, "Flow: A modular learning framework for mixed autonomy traffic," IEEE Transactions on Robotics, vol. 38, no. 2, pp. 1270- 1286, 2021

work page 2021

[5] [3]

Online prediction of lane change with a hierarchical learning -based approach,

X. Liao et al. , "Online prediction of lane change with a hierarchical learning -based approach," in 2022 International Conference on Robotics and Automation (ICRA), 2022: IEEE, pp. 948-954

work page 2022

[6] [4]

Driver Monitoring -Based Lane -Change Prediction: A Personalized Federated Learning Framework,

R. Du, K. Han, R. Gupta, S. Chen, S. Labi, and Z. Wang, "Driver Monitoring -Based Lane -Change Prediction: A Personalized Federated Learning Framework," in 2023 IEEE Intelligent Vehicles Symposium (IV), 2023: IEEE, pp. 1-7

work page 2023

[7] [5]

Lane change decision-making through deep reinforcement learning with rule - based constraints,

J. Wang, Q. Zhang, D. Zhao, and Y . Chen, "Lane change decision-making through deep reinforcement learning with rule - based constraints," in 2019 International Joint Conference on Neural Networks (IJCNN), 2019: IEEE, pp. 1-6

work page 2019

[8] [6]

Value -function approximations for partially observable Markov decision processes,

M. Hauskrecht, "Value -function approximations for partially observable Markov decision processes," Journal of artificial intelligence research, vol. 13, pp. 33-94, 2000

work page 2000